Growth of cod (<i>Gadus morhua</i>) in the western Baltic Sea: estimating improved growth parameters from tag–recapture data
Bibliographic record
Abstract
Tag–recapture data contain valuable information about individual fish growth, which can enhance the estimation of growth parameters. Tag–recapture data are especially useful when age-determination uncertainties throw age-based growth estimates and stock assessments into question, as is the present situation with several important gadoid stocks. Length-based (GROTAG method) and age-based (LEP method) growth functions were fit to a large data set of tagged and recaptured cod (Gadus morhua) from an artificial reef in the western Baltic Sea to calculate improved growth parameter estimates. The LEP method allowed integration of different data formats, including tag–recapture, length frequency, and length-at-age, to estimate a more robust and comprehensive growth function (von Bertalanffy growth parameters: L∞ = 154.56 cm, k = 0.11, t0 = –0.13). Two heavily exploited cod stocks inhabit the Baltic Sea, subsisting at the upper thermal and lower salinity limits of the species. Otolith shape analyses indicated that, unexpectedly, individuals from both populations were resident at the reef. Compared with cod populations elsewhere, cod in the western Baltic Sea grow relatively slowly and with weak seasonal fluctuations in growth rates, potentially due to adverse conditions for growth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".