Comparison of virtual population analysis and statistical kill-at-age analysis for a recreational, kill-dominated fishery
Bibliographic record
Abstract
We used simulations to compare the distributions of estimation errors for virtual population analysis using forward calculation (FVPA) and three variants of statistical kill-at-age analysis (KAA). The KAA variants assumed constant, time-blocked, and nonadditive selectivity. Simulations were based on a recreational walleye (Sander vitreus) fishery in Lake Mille Lacs, Minnesota. The focus of our experiments was on how model mis-specification (incorrect assumptions about selectivity for KAA or that kill had no error for FVPA) interacted with the magnitude of measurement errors and fishing mortality. We found that KAA models outperformed FVPA when they assumed the correct selectivity pattern, even when kill was measured without error. Of particular concern was a strong tendency by FVPA to overestimate stock size when kill was measured with substantial error. When KAA was based on an incorrect assumption regarding fishery selectivity and kill was measured with little error, wide distributions of errors and substantial biases sometimes resulted. KAA models that allowed fishery selectivity to change over time performed about as well as a constant-selectivity KAA model when selectivity was constant, and they performed much better when selectivity changed over time. Careful consideration of alternative fishery selectivity models should be a fundamental part of any age-structured assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.083 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".