Selecting crop models for decision making in wheat insurance

In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a crop model for a specific objective, location and implementation scale is a difficult task. A look inside the different crop and soil modules to understand how outputs are obtained might facilitate model choice. The objectives of this paper were (i) to assess the usefulness of crop models to be used within a crop insurance analysis and design and (ii) to select the most suitable crop model for drought risk assessment in semi-arid regions in Spain. For that purpose first, a pre-selection of crop models simulating wheat yield under rainfed growing conditions at the field scale was made, and second, four selected models (Aquacrop, CERES- Wheat, CropSyst and WOFOST) were compared in terms of modelling approaches, process descriptions and model outputs. Outputs of the four models for the simulation of winter wheat growth are comparable when water is not limiting, but differences are larger when simulating yields under rainfed conditions. These differences in rainfed yields are mainly related to the dissimilar simulated soil water availability and the assumed linkages with dry matter formation. We concluded that for the simulation of winter wheat growth at field scale in such semi-arid conditions, CERES-Wheat and CropSyst are preferred. WOFOST is a satisfactory compromise between data availability and complexity when detail data on soil is limited. Aquacrop integrates physiological processes in some representative parameters, thus diminishing the number of input parameters, what is seen as an advantage when observed data is scarce. However, the high sensitivity of this model to low water availability limits its use in the region considered. Contrary to the use of ensembles of crop models, we endorse that efforts be concentrated on selecting or rebuilding a model that includes approaches that better describe the agronomic conditions of the regions in which they will be applied. The use of such complex methodologies as crop models is associated with numerous sources of uncertainty, although these models are the best tools available to get insight in these complex agronomic systems.

Técnicas de machine learning para la detección de la negación en textos clínicos en español

En los últimos años han surgido nuevos campos de las tecnologías de la información que exploran el tratamiento de la gran cantidad de datos digitales existentes y cómo transformarlos en conocimiento explícito. Las técnicas de Procesamiento del Lenguaje Natural (NLP) son capaces de extraer información de los textos digitales presentados en forma narrativa. Además, las técnicas de machine learning clasifican instancias o ejemplos en función de sus atributos, en distintas categorías, aprendiendo de otros previamente clasificados. Los textos clínicos son una gran fuente de información no estructurada; en consecuencia, información no explotada en su totalidad. Algunos términos usados en textos clínicos se encuentran en una situación de afirmación, negación, hipótesis o histórica. La detección de esta situación es necesaria para la estructuración de información, pero a su vez tiene una gran complejidad. Extrayendo características lingüísticas de los elementos, o tokens, de los textos mediante NLP; transformando estos tokens en instancias y las características en atributos, podemos mediante técnicas de machine learning clasificarlos con el objetivo de detectar si se encuentran afirmados, negados, hipotéticos o históricos. La selección de los atributos que cada token debe tener para su clasificación, así como la selección del algoritmo de machine learning utilizado son elementos cruciales para la clasificación. Son, de hecho, los elementos que componen el modelo de clasificación. Consecuentemente, este trabajo aborda el proceso de extracción de características, selección de atributos y selección del algoritmo de machine learning para la detección de la negación en textos clínicos en español. Se expone un modelo para la clasificación que, mediante el algoritmo J48 y 35 atributos obtenidos de características lingüísticas (morfológicas y sintácticas) y disparadores de negación, detecta si un token está negado en 465 frases provenientes de textos clínicos con un F-Score del 73%, una exhaustividad del 66% y una precisión del 81% con una validación cruzada de 10 iteraciones. ---ABSTRACT--- New information technologies have emerged in the recent years which explore the processing of the huge amount of existing digital data and its transformation into knowledge. Natural Language Processing (NLP) techniques are able to extract certain features from digital texts. Additionally, through machine learning techniques it is feasible to classify instances according to different categories, learning from others previously classified. Clinical texts contain great amount of unstructured data, therefore information not fully exploited. Some terms (tokens) in clinical texts appear in different situations such as affirmed, negated, hypothetic or historic. Detecting this situation is necessary for the structuring of this data, however not simple. It is possible to detect whether if a token is negated, affirmed, hypothetic or historic by extracting its linguistic features by NLP; transforming these tokens into instances, the features into attributes, and classifying these instances through machine learning techniques. Selecting the attributes each instance must have, and choosing the machine learning algorithm are crucial issues for the classification. In fact, these elements set the classification model. Consequently, this work approaches the features retrieval as well as the attributes and algorithm selection process used by machine learning techniques for the detection of negation in clinical texts in Spanish. We present a classification model which, through J48 algorithm and 35 attributes from linguistic features (morphologic and syntactic) and negation triggers, detects whether if a token is negated in 465 sentences from historical records, with a result of 73% FScore, 66% recall and 81% precision using a 10-fold cross-validation.

Ethnogenesis and Craniofacial Change in Japan from the Perspective of Nonmetric Traits

To examine population affinities in light of the ‘dual structure model’, frequencies of 21 nonmetric cranial traits were analyzed in 17 prehistoric to recent samples from Japan and five from continental northeast Asia. Eight bivariate plots, each representing a different bone or region of the skull, as well as cluster analysis of 21-trait mean measures of divergence using multidimensional scaling and additive tree techniques, revealed good discrimination between the Jomon-Ainu indigenous lineage and that of the immigrants who arrived from continental Asia after 300 BC. In Hokkaido, in agreement with historical records, Ainu villages of Hidaka province were least, and those close to the Japan Sea coast were most, hybridized with Wajin. In the central islands, clines were identified among Wajin skeletal samples whereby those from Kyushu most resembled continental northeast Asians, while those from the northernmost prefectures of Tohoku apparently retained the strongest indigenous heritage. In the more southerly prefectures of Tohoku, stronger traces of Jomon ancestry prevailed in the cohort born during the latest Edo period than in the one born after 1870. Thus, it seems that increased inter-regional mobility and gene flow following the Meiji Restoration initiated the most recent episode in the long process of demic diffusion that has helped to shape craniofacial change in Japan.

Evolution of the landscape of Madeira Island: long-term vegetation dynamics

The aim of this thesis was to evaluate historical change of the landscape of Madeira Island and to assess spatial and temporal vegetation dynamics. In current research diverse “retrospective techniques”, such as landscape repeat photography, dendrochronology, and research of historical records were used. These, combined with vegetation relevés, aimed to gather information about landscape change, disturbance history, and vegetation successional patterns. It was found that landscape change, throughout 125 years, was higher in the last five decades manly driven by farming abandonment, building growth and exotic vegetation coverage increase. Pristine vegetation was greatly destroyed since early settlement and by the end of the nineteenth century native vegetation was highly devastated due to recurrent antropogenic disturbances. These actions also helped to block plant succession and to modify floristical assemblages, affecting as well as species richness. In places with less hemeroby, although significant growth of vegetation of lower seral stages was detected, the vegetation of most mature stages headed towards unbalance between recovery and loss, being also very vulnerable to exotic species encroachment. Recovery by native vegetation also occurred in areas formerly occupied by exotic plants and agriculture but it was almost negligible. Vegetation recovery followed the successional model currently proposed, attesting the model itself. Yet, succession was slower than espected, due to lack of favourable conditions and to recurrent disturbances. Probable tempus of each seral stage was obtained by growth rates of woody taxa estimated through dendrochronology. The exotic trees which were the dominant trees in the past (Castanea sativa and Pinus pinaster) almost vanished. Eucalyptus globulus, the current main tree of the exotic forest is being replaced by other cover types as Acacia mearnsii. The latter, along with Arundo donax, Cytisus scoparius and Pittosporum undulatum are currently the exotic species with higher invasive behaviour. However, many other exotic species have also proved to be highly pervasive and came together with the ones referred above to prevent native vegetation regeneration, to diminish biological diversity, and to block early successional phases delaying native forest recovery.

Calendar of Alexander Graham Bell correspondence in the Volta bureau,

Mode of access: Internet.

Inventory of the county archives of Minnesota.

59. Pipestone County -- 64. Redwood County -- 65. Renville County -- 66. Rice County -- 67. Rock County -- 70. Scott County -- 71. Sherburne County -- 73. Stearns County -- 78. Traverse County -- 79. Wabasha County -- 82. Washington County -- 86. Wright County -- 87. Yellow Medicine County

Inventory of the county archives of Maryland.

Mimeographed, no. 1-

The atlas of congressional roll calls ..

Clifford Lee Lord, editor.

Inventory of the county archives of Ohio.

Mimeographed.

Inventory of the municipal archives of Ohio.

Reproduced from typewritten copy.

American imprints inventory.

Mimeographed; cover printed.

American imprints inventory,

Mode of access: Internet.

A history of Ogden; pre-print of the proposed inventory of municipal archives of Utah,

"Ogden city bibliography": p. 69-77.

Census of Weber County (excluding Green River precinct) provisional state of Deseret 1850,

"The names in this booklet have been copied verbatim from the schedule sheets compiled by E. D. Rich, assistant marshall ... Hugh O'Neil, project editor, supervised the publication of this booklet."--2d prelim. leaf.

American portraits, 1620-1825, found in Massachusetts ...

Paged continuously.