Deriving condition indices from standard fisheries databases and evaluating their sensitivity to variation in stored energy reserves
Bibliographic record
Abstract
To evaluate interstock differences in condition, it would be advantageous to develop stock-level condition indices from standardized databases on weight and length. This study describes a method for estimating stock-level condition when individual-level observations on length and weight are not easily accessible. For each year in a 56-year time series (19462001) for Northeast Arctic cod (Gadus morhua), pseudo-observations of weight and length were generated by pairing the Norwegian and Russian values for weight-at-age provided annually to the assessment working group with estimates of length-at-age derived from the same databases. A weightlength relationship fit to each year was then used to predict weight-at-length, i.e., girth, for a range of standard lengths (30120 cm). This index was uncorrelated with both the liver condition index and the abundance of Barents Sea capelin (Mallotus villosus), suggesting that at the stock level, the girth of cod is not necessarily indicative of the magnitude of stored energy reserves. Partitioning body size into length-at-age and girth revealed long-term trends in body size. In particular, large/old cod showed substantially higher values of both length-at-age and girth that could be the result of long-term increases in fishing mortality.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".