Using an individual-based model of fish assemblages to study the response of size spectra to changes in fishing
Bibliographic record
Abstract
For most fish species, strong environmental constraints imposed by living in an aquatic medium have produced converging streamlined body forms without prehensile appendices. This similarity in body shapes highlights a common predation constraint: a predatory fish must have a jaw large enough to swallow its prey. Fish diets may then reflect local prey availability and predatorprey size ratios. Based on this size-based opportunistic predation process, the multispecies individual-based model OSMOSE (Object-oriented Simulator of Marine ecOSystem Exploitation) is used to investigate to what extent the size distribution of fish communities can contribute to better our understanding of the functioning of marine food webs and the ecosystem effects of fishing. Strong similarity in shape is found between simulated size spectra and those described in empirical studies. The existence of a curvature towards small size classes is discussed in the light of the size-based predation hypothesis, which implies that smaller fish may undergo higher predation mortality. Applying linear and quadratic regressions to the simulated size spectra allows the detection of variations in fishing pressure and the proposal of different ways to quantify them. In particular, it is shown that the slope of the size spectrum decreases quasilinearly with fishing mortality and that the curvature could help to detect ecosystem overexploitation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".