Control óptimo y manejo de pesquerías I: Marco conceptual y métodos formales
Bibliographic record
Abstract
Se introducen en esta primera parte las ideas generales que hacen posible la aplicacion de metodos de optimizacion dinamica al manejo de un pesqueri que explota de modo fundamental a una poblacion uniespecifica. Se presenta una derivacion heuristica de la ecuacion de Bellman de la programacion dinamica. Un tratamiento similar es utilizado para establecer el principio maximo de Pontrygin. Este se us para obtener la estrategia de control que maximiza el beneficio social derivado de la explotacion. La optimalidad de dicha estrategia se corrobora en un primer apendice mediante el uso de procedimientos directos. La aplicacion de metodos de programacion dinamica para obtener la estrategia arriba mencionada en el caso autonomo y bajo la aplicacion de controles de impulso se presenta en un segundo apendice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".