Estimating predation mortality in the Georges Bank fish community
Bibliographic record
Abstract
Multispecies virtual population analysis (MSVPA) is one of the most successful methods of including predation in fishery models. By applying MSVPA to nine important fish species on Georges Bank, we estimated predation mortality of prey species, fishing mortality, and population abundance from 1978 to 1992. One of the inputs to the MSVPA, relative stomach content, was estimated by fitting gamma distributions to the logarithmic predator-to-prey size ratios. Chi-square tests indicated that the gamma distributions fit the observed ratios well. Predation mortality was highest at ages 0 and 1. Total biomass of all species remained relatively constant with decreasing predator biomass and increasing prey biomass. MSVPA requires extensive input data, and the uncertainty in the inputs will propagate into the model output. The sensitivity of MSVPA to perturbations in the inputs was assessed with a two-level fractional factorial design. Results of the sensitivity test indicated that MSVPA outputs were most sensitive to predator consumption rates and terminal fishing mortalities. With ±25% perturbations to the input parameters, MSVPA outputs varied within ±10% of the levels from the base run. Therefore, MSVPA appears to be relatively robust to uncertainty in the input data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".