Linking humans to food webs: a framework for the classification of global fisheries
Bibliographic record
Abstract
There is a widely acknowledged need to explicitly include humans in our conceptual and mathematical models of food webs. However, a simple and generalized method for incorporating humans into fisheries food webs has yet to be established. We developed a simple graphical framework for defining whole‐system inland fishery food webs that includes a continuum of fishery behaviors. This range of behaviors mimics those of generalist to specialist predators, which differentially influence ecosystem diversity, sustainability, and functioning. Fishery behaviors in this food‐web context are predicted to produce a range of “fishery types” – from targeted (ie specialist) to multispecies (ie generalist) inland fisheries – and relate to the socioeconomic status of fishery participants. Fishery participants in countries with low Human Development Index (HDI) values are highly connected through fisheries food webs relative to humans in more developed countries. Our framework shows that fisheries can occupy a variety of roles within a food‐web model and may thereby affect food‐web stability in different ways. This realization could help to improve sustainable fisheries management at a global scale.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".