Use of Geographic Information Systems by Fisheries Management Agencies
Bibliographic record
Abstract
ABSTRACT Use of geographic information systems (GIS) in fisheries science has increased in prevalence since its introduction in the late 1980s, but use among and within fisheries management agencies has not been quantified. We surveyed 89 administrators of fisheries management agencies in the United States and Canada to determine the current status of GIS in fisheries management and received 54 responses (61% return rate). Survey respondents indicated that GIS was used to help manage fish populations, and 63% of respondents believed that GIS was either “very useful” or “extremely useful” for meeting agency objectives. However, most GIS work conducted by fisheries management agencies was executed by few individuals within the agency or by contracted service. Barriers preventing more widespread use by managers within agencies included lack of knowledge or training and limited time to use GIS in job duties. Our results suggest that GIS is an important tool for fisheries management. Further, GIS use within an agency might be increased by focusing on increased biologist participation in training exercises, integration with existing job duties, and recognizing diversity among GIS software. RESUMEN desde que se introdujo en la década de los ochenta, los Sistemas de Información Geográfica (GIS) se han utilizado cada vez más en las ciencias pesqueras, sin embargo aún no se ha cuantificado dicha utilización entre y hacia el interior de las agencias de manejo pesquero. Se realizó un sondeo a 89 encargados de agencias manejo pesquero en los Estados Unidos de Norteamérica y Canadá para determinar el estado actual de los GIS en el ámbito de manejo, recibiéndose 54 respuestas (tasa de retorno de 61%). Los encuestados indicaron que los GIS fueron desde “muy útiles” hasta “extremadamente útiles” para cumplir los objetivos de las agencias. No obstante, la mayor parte del trabajo de las agencias basado en los GIS, era realizado por algunos pocos individuos de las propias agencias o bien por servicios contratados. Las barreras que impiden un uso más generalizado de los GIS hacia el interior de las agencias de manejo incluyen la falta de conocimiento o entrenamiento y limitaciones de tiempo para usar este enfoque como parte del trabajo diario. Estos resultados sugieren que los GIS son una herramienta importante para el manejo de pesquerías. Más aún, el uso de los GIS dentro de las agencias, pudiera incrementarse enfocando o aumentando la participación de los biólogos en los ejercicios de entrenamiento, integración a las labores diarias de trabajo y reconociendo la diversidad de software de los GIS.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".