A multispecies age-structured assessment model for the Gulf of Alaska
Bibliographic record
Abstract
Predation is the largest source of mortality for marine fish and may be an important process in regulating population size. Recent population models have attempted to quantify predation separately from other sources of natural mortality. Building upon such work, a multispecies age-structured assessment model (MSASA) for the Gulf of Alaska was developed, which included arrowtooth flounder ( Atheresthes stomias ), Pacific cod ( Gadus macrocephalus ), and walleye pollock ( Theragra chalcogramma ). Predation mortality was a flexible function of predator and prey abundances that was fitted to stomach-content data. A proof of concept illustration is presented here, assessing model outputs against a set of single-species models. The MSASA model was able to successfully estimate predation between species and integrate it into total mortality. Significant predation occurred on younger pollock and flounder. Results indicate a significant change in predation over time on pollock as a function of increased arrowtooth flounder abundance. Estimating mortality and other parameters for three species simultaneously is complex, and the advantage of greater biological realism of MSASA comes at the expense of greater uncertainty in parameter estimation.
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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 | 0.002 |
| 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) | 0.004 | 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".