Subsampling populations with spatially structured traits: a field comparison of stratified and random strategies
Bibliographic record
Abstract
Scientific surveys are widely used for stock assessment, but the estimated population parameters are based on the size-at-age relationship and age structure derived from a small subsample of the catch that is aged. This calls for an assessment of subsampling strategies, especially when population’s life history traits are spatially structured. In the Eastern Bering Sea, Pacific cod (Gadus macrocephalus) size and age are spatially structured, with younger and smaller individuals being more abundant at shallower depths. We conducted parallel subsamplings during Pacific cod surveys to compare two contrasting subsampling strategies: length-stratified and random. Geographical heterogeneity of Pacific cod length resulted in divergent estimates of ages between subsampling strategies. When this spatial variability was taken into account to estimate population parameters, random strategy provided more accurate mean and modal size-at-age and estimated age structure. Bias in the length-stratified subsampling arises from the poor efficacy in capturing the geographical patterns of size observed in the population. However, combining age data samples from multiple years helps to minimize the divergences between the two strategies.
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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 | 0.063 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".