Estudio de la problemática de la caracterización del olivar mediante sensores remotos
Bibliographic record
Abstract
La Tesis plantea la problematica que presenta la identificacion fiable de los cultivos de olivar a partir de imagenes Landsat-TM, Con este tipo de imagenes, en las que la resolucion espacial no permite reconocer por su forma las copas de los arboles, lo usual es el analisis multiespectral recurriendo a tecnicas de clasificacion.Pero los marcos tradicionales de cultivo en los olivares del sur de Espana dejan descubierta una gran proporcion de suelo desnudo, por lo que la contribucion radiometrica de este prevalece sobre la del material vegetal, lo que termina repercutiendo en una identificacion deficiente. El trabajo desarrolla un procedimiento de normalizacion adiometrica, que el autor denomina vegetalizacion, basado en un modelo geometrico de reflectancia desarrollado expresamente para el olivar, en el que se deduce la componente de reflectancia exclusivamente debida al cultivo en funcion de la reflectancia total y la del suelo desnudo, asi como de la topografia del terreno y su orientacion relativa con respecto a la situacion instantanea del Sol y del sensor. La clasificacion supervisada de las imagnes normalizadas por vegetalizacion proporcionan resultados significativamente mejores que los obtenidos a partir de la imagen original o incluso de la imagen normalizada topograficamente siguiendo los procedimientos.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".