Primary productivity demands of global fishing fleets
Bibliographic record
Abstract
Abstract To be sustainable, the extractive process of fishing requires biomass renewal via primary production driven by solar energy. Primary production required ( PPR ) estimates how much primary production is needed to replace the biomass of fisheries landings removed from marine ecosystems. Here, we examine the historical fishing behaviour of global fishing fleets, which parts of the food web they rely on, which ecosystems they fish and how intensively. Highly mobile E uropean and A sian fleets have moved to ever more distant productive waters since the 1970s, especially once they are faced with the costs of access agreements for exclusive economic zones ( EEZ s) declared by host countries. We examine fleet PPR demands in the context of large marine ecosystems ( LME s), which are frequently fished with PPR demands well above their average primary productivity ( PP ). In some cases, this was mitigated by subsequent emigration of fleets or by management intervention. Fleet movements, however, have stressed additional marine areas, including the EEZ s of developing countries. This suggests the potential for spatial serial depletion, if fishing capacity is not reduced to more sustainable PP removal levels. Fundamentally, fishing is limited by solar‐powered PP limits. Fishing beyond solar production has occurred, but in the future, marine systems may not be as forgiving, especially if overfishing and climate change compromise their resilience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".