Pragmatic solutions for syntactic problems: Understanding some L2 syntactic errors in terms of discourse-pragmatic deficits

Contemporary research in generative second language (L2) acquisition has attempted to address observable target-deviant aspects of L2 grammars within a UG-continuity framework (e.g. Lardiere 2000; Schwartz 2003; Sprouse 2004; Prévost & White 1999, 2000). With the aforementioned in mind, the independence of pragmatic and syntactic development, independently observed elsewhere (e.g. Grodzinsky & Reinhart 1993; Lust et al. 1986; Pacheco & Flynn 2005; Serratrice, Sorace & Paoli 2004), becomes particularly interesting. In what follows, I examine the resetting of the Null-Subject Parameter (NSP) for English learners of L2 Spanish. I argue that insensitivity to associated discoursepragmatic constraints on the discursive distribution of overt/null subjects accounts for what appear to be particular errors as a result of syntactic deficits. It is demonstrated that despite target-deviant performance, the majority must have native-like syntactic competence given their knowledge of the Overt Pronoun Constraint (Montalbetti 1984), a principle associated with the Spanish-type setting of the NSP.

Sea-breeze dynamics and convection initiation: the influence of convective parameterization in weather and climate model biases

There are some long-established biases in atmospheric models that originate from the representation of tropical convection. Previously, it has been difficult to separate cause and effect because errors are often the result of a number of interacting biases. Recently, researchers have gained the ability to run multiyear global climate model simulations with grid spacings small enough to switch the convective parameterization off, which permits the convection to develop explicitly. There are clear improvements to the initiation of convective storms and the diurnal cycle of rainfall in the convection-permitting simulations, which enables a new process-study approach to model bias identification. In this study, multiyear global atmosphere-only climate simulations with and without convective parameterization are undertaken with the Met Office Unified Model and are analyzed over the Maritime Continent region, where convergence from sea-breeze circulations is key for convection initiation. The analysis shows that, although the simulation with parameterized convection is able to reproduce the key rain-forming sea-breeze circulation, the parameterization is not able to respond realistically to the circulation. A feedback of errors also occurs: the convective parameterization causes rain to fall in the early morning, which cools and wets the boundary layer, reducing the land–sea temperature contrast and weakening the sea breeze. This is, however, an effect of the convective bias, rather than a cause of it. Improvements to how and when convection schemes trigger convection will improve both the timing and location of tropical rainfall and representation of sea-breeze circulations.

The timing of anthropogenic emergence in simulated climate extremes

Determining the time of emergence of climates altered from their natural state by anthropogenic influences can help inform the development of adaptation and mitigation strategies to climate change. Previous studies have examined the time of emergence of climate averages. However, at the global scale, the emergence of changes in extreme events, which have the greatest societal impacts, has not been investigated before. Based on state-of-the-art climate models, we show that temperature extremes generally emerge slightly later from their quasi-natural climate state than seasonal means, due to greater variability in extremes. Nevertheless, according to model evidence, both hot and cold extremes have already emerged across many areas. Remarkably, even precipitation extremes that have very large variability are projected to emerge in the coming decades in Northern Hemisphere winters associated with a wettening trend. Based on our findings we expect local temperature and precipitation extremes to already differ significantly from their previous quasi-natural state at many locations or to do so in the near future. Our findings have implications for climate impacts and detection and attribution studies assessing observed changes in regional climate extremes by showing whether they will likely find a fingerprint of anthropogenic climate change.

Systematic and random errors between collocated satellite ice water path observations

There remains large disagreement between ice-water path (IWP) in observational data sets, largely because the sensors observe different parts of the ice particle size distribution. A detailed comparison of retrieved IWP from satellite observations in the Tropics (!30 " latitude) in 2007 was made using collocated measurements. The radio detection and ranging(radar)/light detection and ranging (lidar) (DARDAR) IWP data set, based on combined radar/lidar measurements, is used as a reference because it provides arguably the best estimate of the total column IWP. For each data set, usable IWP dynamic ranges are inferred from this comparison. IWP retrievals based on solar reflectance measurements, in the moderate resolution imaging spectroradiometer (MODIS), advanced very high resolution radiometer–based Climate Monitoring Satellite Applications Facility (CMSAF), and Pathfinder Atmospheres-Extended (PATMOS-x) datasets, were found to be correlated with DARDAR over a large IWP range (~20–7000 g m -2 ). The random errors of the collocated data sets have a close to lognormal distribution, and the combined random error of MODIS and DARDAR is less than a factor of 2, which also sets the upper limit for MODIS alone. In the same way, the upper limit for the random error of all considered data sets is determined. Data sets based on passive microwave measurements, microwave surface and precipitation products system (MSPPS), microwave integrated retrieval system (MiRS), and collocated microwave only (CMO), are largely correlated with DARDAR for IWP values larger than approximately 700 g m -2 . The combined uncertainty between these data sets and DARDAR in this range is slightly less MODIS-DARDAR, but the systematic bias is nearly an order of magnitude.

High-precision measurements of the co-polar correlation coefficient: non-Gaussian errors and retrieval of the dispersion parameter µ in rainfall

The co-polar correlation coefficient (ρhv) has many applications, including hydrometeor classification, ground clutter and melting layer identification, interpretation of ice microphysics and the retrieval of rain drop size distributions (DSDs). However, we currently lack the quantitative error estimates that are necessary if these applications are to be fully exploited. Previous error estimates of ρhv rely on knowledge of the unknown "true" ρhv and implicitly assume a Gaussian probability distribution function of ρhv samples. We show that frequency distributions of ρhv estimates are in fact highly negatively skewed. A new variable: L = -log10(1 - ρhv) is defined, which does have Gaussian error statistics, and a standard deviation depending only on the number of independent radar pulses. This is verified using observations of spherical drizzle drops, allowing, for the first time, the construction of rigorous confidence intervals in estimates of ρhv. In addition, we demonstrate how the imperfect co-location of the horizontal and vertical polarisation sample volumes may be accounted for. The possibility of using L to estimate the dispersion parameter (µ) in the gamma drop size distribution is investigated. We find that including drop oscillations is essential for this application, otherwise there could be biases in retrieved µ of up to ~8. Preliminary results in rainfall are presented. In a convective rain case study, our estimates show µ to be substantially larger than 0 (an exponential DSD). In this particular rain event, rain rate would be overestimated by up to 50% if a simple exponential DSD is assumed.

Timing and nature of the penultimate deglaciation in a high alpine stalagmite from Switzerland

The timing and nature of the penultimate deglaciation, also known as Termination II (T-II), is subject of controversial discussions due to the scarcity of precisely-dated palaeoclimate records. Here we present a new precisely-dated and highly-resolved multi-proxy stalagmite record covering T-II from the high alpine Schafsloch Cave in Switzerland, an area where climate is governed by the North Atlantic. The inception of stalagmite growth at 137.4 ± 1.4 kyr before present (BP) indicates the presence of drip water and cave air temperatures of above 0°C, and is related to a climate-induced change in the thermal state (from cold- to warm-based) of the glacier above the cave. The cessation of stalagmite growth between 133.1 ± 0.7 and 131.9 ± 0.6 kyr BP is most likely related to distinct drop in temperature associated with Heinrich stadial 11. The resumption of stalagmite growth at 131.9 ± 0.6 kyr BP is accompanied by an abrupt increase in temperature and precipitation as indicated by distinct shifts in the oxygen and carbon isotopic composition as well as in trace element concentrations. The mid-point of T-II is around 131.8 ± 0.6 kyr BP in the Schafsloch Cave record is significantly earlier compared to the age of 129.1 ± 0.1 kyr BP in the Sanbao Cave record from China. The different ages between both records can be best explained by the competing effects of insolation and glacial boundary forcing on seasonality and snow cover extent in Eurasia.

Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants

Ruminant husbandry is a major source of anthropogenic greenhouse gases (GHG). Filling knowledge gaps and providing expert recommendation are important for defining future research priorities, improving methodologies and establishing science-based GHG mitigation solutions to government and non-governmental organisations, advisory/extension networks, and the ruminant livestock sector. The objectives of this review is to summarize published literature to provide a detailed assessment of the methodologies currently in use for measuring enteric methane (CH4) emission from individual animals under specific conditions, and give recommendations regarding their application. The methods described include respiration chambers and enclosures, sulphur hexafluoride tracer (SF6) technique, and techniques based on short-term measurements of gas concentrations in samples of exhaled air. This includes automated head chambers (e.g. the GreenFeed system), the use of carbon dioxide (CO2) as a marker, and (handheld) laser CH4 detection. Each of the techniques are compared and assessed on their capability and limitations, followed by methodology recommendations. It is concluded that there is no ‘one size fits all’ method for measuring CH4 emission by individual animals. Ultimately, the decision as to which method to use should be based on the experimental objectives and resources available. However, the need for high throughput methodology e.g. for screening large numbers of animals for genomic studies, does not justify the use of methods that are inaccurate. All CH4 measurement techniques are subject to experimental variation and random errors. Many sources of variation must be considered when measuring CH4 concentration in exhaled air samples without a quantitative or at least regular collection rate, or use of a marker to indicate (or adjust) for the proportion of exhaled CH4 sampled. Consideration of the number and timing of measurements relative to diurnal patterns of CH4 emission and respiratory exchange are important, as well as consideration of feeding patterns and associated patterns of rumen fermentation rate and other aspects of animal behaviour. Regardless of the method chosen, appropriate calibrations and recovery tests are required for both method establishment and routine operation. Successful and correct use of methods requires careful attention to detail, rigour, and routine self-assessment of the quality of the data they provide.

The timing of Neotropical speciation dynamics: A reconstruction of Myiopagis flycatcher diversification using phylogenetic and paleogeographic data

Neotropical forests have brought forth a large proportion of the world`s terrestrial biodiversity, but the underlying evolutionary mechanisms and their timing require further elucidation. Despite insights gained from phylogenetic studies, uncertainties about molecular clock rates have hindered efforts to determine the timing of diversification processes. Moreover, most molecular research has been detached from the extensive body of data on Neotropical geology and paleogeography. We here examine phylogenetic relationships and the timing of speciation events in a Neotropical flycatcher genus (Myiopagis) by using calibrations from modern geologic data in conjunction with a number of recently developed DNA sequence dating algorithms and by comparing these estimates with those based on a range of previously proposed molecular clock rates. We present a well-supported hypothesis of systematic relationships within the genus. Our age estimates of Myiopagis speciation events based on paleogeographic data are in close agreement with nodal ages derived from a ""traditional"" avian mitochondrial 2%/My clock, while contradicting other clock rates. Our comparative approach corroborates the consistency of the traditional avian mitochondrial clock rate of 2%/My for tyrant-flycatchers. Nevertheless, our results argue against the indiscriminate use of molecular clock rates in evolutionary research and advocate the verification of the appropriateness of the traditional clock rate by means of independent calibrations in individual studies. (C) 2009 Elsevier Inc. All rights reserved.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

A multivariate ultrastructural errors-in-variables model with equation error

This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.

Influence diagnostics for linear models with first-order autoregressive elliptical errors

We introduce in this paper the class of linear models with first-order autoregressive elliptical errors. The score functions and the Fisher information matrices are derived for the parameters of interest and an iterative process is proposed for the parameter estimation. Some robustness aspects of the maximum likelihood estimates are discussed. The normal curvatures of local influence are also derived for some usual perturbation schemes whereas diagnostic graphics to assess the sensitivity of the maximum likelihood estimates are proposed. The methodology is applied to analyse the daily log excess return on the Microsoft whose empirical distributions appear to have AR(1) and heavy-tailed errors. (C) 2008 Elsevier B.V. All rights reserved.