A database of mapped global fishing activity 1950–2017
Bibliographic record
Abstract
A new database on historical country-level fishing fleet capacity and effort is described, derived from a range of publicly available sources that were harmonized, converted to fishing effort, and mapped to 30-min spatial cells. The resulting data is comparable with widely used but more temporally-limited satellite-sourced Automatic Identification System (AIS) datasets for large vessels, while also documenting important smaller fleets and artisanal segments. It ranges from 1950 to 2017, and includes information on number of vessels, engine power, gross tonnage, and nominal effort, categorized by vessel length, gear type and targeted functional groups. The data can be aggregated to Large Marine Ecosystem, region and/or fishing country scales and provides a temporally and spatially explicit source for fishing effort and fleet capacity for studies aimed at understanding the implications of long-term changes in fishing activity in the global ocean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads agree on what is shown here.
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".