Sampling selectivity in acoustic-trawl surveys of Pacific sardine (Sardinops sagax) biomass and length distribution†
Bibliographic record
Abstract
Abstract Demer, D. A., Zwolinski, J. P., Cutter, G. R. Jr, Byers, K. A., Macewicz, B. J., and Hill, K. T. Sampling selectivity in acoustic-trawl surveys of Pacific sardine (Sardinops sagax) biomass and length distribution. – ICES Journal of Marine Science, 70: . To annually assess the northern stock of Pacific sardine (Sardinops sagax) in the California Current and set harvest quotas for the US fishery, managers have used an age-structured stock synthesis model fitted with results from acoustic-trawl (ATM), daily-egg-production, and aerial-photogrammetric survey methods, fishery landing and individual-length data, and many assumed or empirically derived parameters. In these assessments, sardine landed at ports spanning from Ensenada, México to Vancouver Island, Canada were assumed to be solely from the northern stock. It was also assumed that the ATM estimates of sardine biomass were negligibly biased for the sizes of fish sampled by the survey trawls (i.e., catchability q = 1 for sardine standard length (SL) values greater than ∼17 cm). Due to these catchability and length-selectivity assumptions, the ATM- and assessment-estimated abundances are mostly similar for larger sardine. However, the assessment estimates include large abundances of small sardine (SL values less than ∼15 cm) that are not represented in either the ATM-survey results or the fishery landings, and generally did not recruit to the migrating northern stock sampled by the ATM surveys. We considered four explanations for this disparity: (i) the ATM length-selectivity assumption is correct; (ii) the non-recruiting small fish may comprise a smaller portion of the stock than indicated by the assessments; (iii) during years of low recruitment success, those size classes may be virtually completely fished by the Ensenada and San Pedro fisheries; or (iv) they may belong to the southern sardine stock. This investigation emphasizes the previously identified importance of differentiating samples from the northern and southern stocks and surveying their entire domains.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".