Measurements of natural mortality for Pacific sardine (Sardinops sagax)
Bibliographic record
Abstract
Abstract Zwolinski, J. P., and Demer, D. A. 2013. Measurements of natural mortality for Pacific sardine (Sardinops sagax). – ICES Journal of Marine Science, 70: . The northern stock of Pacific sardine (Sardinops sagax) in the California Current Ecosystem is periodically assessed to provide harvest guidelines for the United States and Canadian fisheries. The assessment model incorporates the estimates of abundance from acoustic-trawl method (ATM) and other surveys and many parameters including a constant value for the instantaneous natural mortality coefficient (M). Here, the ATM-estimated length-structured sardine abundances for 2006–2011 are combined to directly estimate M. The stock was dominated by the 2003, 2004, and 2005 year classes (2003–2005 cohort) from 2006 to 2010 and by the 2009 and 2010 year classes in 2011. Insignificant recruitments during 2006–2010 allowed the 2003–2005 cohort to be tracked and M to be estimated. The bootstrap distributions of estimated M encompass the assessment-assumed value of 0.4−year. The measurements suggest that M is high for a new cohort, declines during mid-life, then increases as the fish approach their longevity. The mean M for the entire period, 0.52, is 30% higher than the one currently assumed for the assessment, reflecting the steeper decline in the ATM-estimated biomass versus that in the assessment model.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".