Coastal catch transects as a tool for studying global fisheries
Bibliographic record
Abstract
Abstract We present a new, intuitive approach for the representation of fisheries catches within profiles perpendicular to coast of the Exclusive Economic Zones (EEZ) of countries, or of Large Marine Ecosystems (LME). These ‘catch transects’ show where catch is extracted in the water column and near the sea bottom on plots of log‐bathymetry versus log‐distance offshore and thus allow for representation of the catch density of pelagic and benthic fisheries. Hence, they also allow direct visual comparison of the intensity of fishing through time and space. The California Current, North Sea and the South China Sea LMEs and the EEZs of Australia, Canada, Chile, China, India and Thailand are presented as examples, revealing the general intensification and extension of fishing offshore and into the depths over the decades from the 1950s. Catch transects reveal how these trends have accelerated in some areas, but surprisingly have reversed themselves in some others. It is proposed that these catch transects will be particularly useful for communicating the results of large‐scale fisheries studies to a wide spectrum of groups ranging from the fishing industry to the general public.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".