El potencial de los sistemas de información geográfica para la geografia: el ejemplo de la Universidad de Lleida
Bibliographic record
Abstract
En primer lugar, se nos describe la experiencia de la implantación de los Sistemas de Información Geográfica en el Departamento de Geografía y Sociología de la Universidad de Lleida. Posteriormente se desarrollan con más detalle dos proyectos recientes donde se ha trabajado con especial énfasis la accesibilidad y la centralidad como aspectos determinantes para la gestión y planificación del territorio regional o local. Las fórmulas utilizadas para dichos cálculos siguen siendo los mismos que se utilizaban tradicionalmente, pero la diferencia radica en la automatización de los cálculos gracias a los SIGs. Estas tecnologías permiten reducir el coste de producción, aumentar la eficacia y representar de forma sencilla los resultados en mapas. Con estas exposiciones, asimismo, la autora pretende reivindicar el papel del geógrafo como científico.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".