Metodología para evaluar la efectividad del manejo de áreas protegidas marinas/A New Methodology for Assessing the Effectiveness of Marine Protected Areas
Bibliographic record
Abstract
Management effectiveness is normally accepted as the degree to which a Marine Protected Area (MPA) is used to achieve its goals and objectives. Prior to 1993, an assessment of whether or not the establishment and management of MPA had achieved their goals was lacking. Instead there were only a few papers dealing with MPA objectives and benefits and they concentrated on biological conditions alone, and therefore were not comprehensive in nature. This study produced a new methodology for assessing the management effectiveness of a given MPA and applied it to a real case study. The methodology constitutes an aggregated, interdisciplinary metric of MPA effectiveness that can lead to improvements in the management of MPAs worldwide by providing quantitative feedback on the ability any one or combination of management interventions to achieve multiple objectives. The methodology constitutes an advancement that draws on other contributions to produce a new approach that goes beyond what has been done previously in evaluating MPA effectiveness. Key words: management effectiveness, marine protected areas. RESUMEN La efectividad de manejo se entiende como el grado en que los objetivos de manejo de un area protegida han sido cumplidos. Antes del 1993 no existian en la literatura trabajos sobre este tema, y solo se encontraban articulos que trataban sobre los objetivos y beneficios de las areas protegidas vistos desde una perspectiva biologica solamente. En el presente trabajo se desarrolla una nueva metodologia para evaluar la efectividad de manejo de un area marina protegida y la misma se aplica a un estudio de caso real. La metodologia propuesta constituye una herramienta interdisciplinaria que pudiera mejorar el manejo de las areas marinas protegidas. Esta metodologia constituye un paso adelante que se nutre de las contribuciones anteriores y provee un nuevo enfoque a aplicar que va mas alla de lo que se ha logrado hasta el presente en esta tematica. Palabras clave: efectividad de manejo, areas marinas protegidas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".