Linking real world fisheries datasets for mapping of revenue from fishing grounds to dependent communities
Bibliographic record
Abstract
Mapping the economic value of the ocean is pivotal to understand how marine ecosystems contribute to human well-being and to support fisheries management. We present a framework to analyse fisheries data and map fishing revenues by linking Vessel Monitoring System (VMS) information to logbooks and observer data. We provide a detailed step-by-step methodology and describe different approaches available to fulfill each step, with special notes for the processing of real-world messy data. The framework consists of six processing steps: (1) identifying the target fishery and subsetting VMS data, (2) extracting relevant variables, (3) linking observer and VMS data, (4) identifying fishing activity, (5) linking VMS and logbook data, and (6) extracting derived variables and mapping revenue back to the communities that extracted the resources. Building this framework opens a broad range of applications including marine spatial planning, rapid response analyses, high-resolution stock assessments, and spatially explicit-socioeconomic analyses. We demonstrate the framework in the reef fishery of the Gulf of Mexico, where spatial planning for aquaculture is currently underway.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".