Se presenta el análisis a priori de un cuestionario sobre divisibilidad de números enteros, mediante Análisis Estadístico Implicativo. Para realizarlo se construyó una matriz booleana en cuyas filas se consignaron los saberes requeridos para resolver los ítems en evaluación; éstos constituyen las columnas de la matriz Luego se realizó un análisis de similaridad de las variables determinándose cuatro clases de cuasi-equivalencia. Finalmente se construyó el grafo implicativo correspondiente a cada una de ellas a fin de establecer que relaciones de tipo causa-efecto existen entre las variables. Este estudio servirá de referencia para analizar, con la misma metodología, las respuestas de los alumnos que sean evaluados con este instrumento.Palabras clave: Análisis Estadístico Implicativo - Análisis a priori – Divisibilidad - Matriz MAP - Árbol de similaridad - Cuasi-implicaciones.
The ground‐penetrating radar (GPR) method with the standard constant‐offset reflection mode allows to detect and map different types of archaeological structures, such as walls, foundations, floors and roads. The interpretation of the GPR data usually involves a detailed and time‐consuming analysis of large amounts of information, which entails nonnegligible chances of errors, especially under nonideal fieldwork conditions. The application of suitable automatic detection algorithms can be useful to more rapidly and successfully complete the interpretation task. In this work, we explore the use of supervised machine learning methodologies to automatically detect mud‐wall signatures in radargrams and to map the structures from these detections. Several algorithms, based on Viola–Jones cascade classifiers and the image feature descriptors Haar, histogram of oriented gradients and local binary patterns, were implemented. These algorithms were applied to datasets previously acquired in pre‐Inca and Inca‐Hispanic sites located in the Andean NW region of Argentina. The best algorithms provided very good detection rates for well‐preserved walls and acceptable rates for deteriorated walls, with a low number of spurious predictions. They also exhibited the ability to detect collapsed walls and fragments detached from them. These are remarkable results because mud walls are usually difficult to be detected by conventional analysis, owing to the complex and variable characteristics of their reflection patterns. The results of the automatic detection techniques were represented in plan views and three‐dimensional (3D) views that delineated in detail most of the structures of the sites. These algorithms are very fast, so applying them significantly reduces the interpretation times. In addition, once they have been trained using the patterns of one or several sites, they are directly applicable to other sites with similar characteristics.
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