Relationships between maternal body size, condition and potential fecundity of four north‐west Atlantic demersal fishes
Bibliographic record
Abstract
Fecundity data for four species (American plaice Hippoglossoides platessoides, yellowtail flounder Limanda ferruginea, witch flounder Glyptocephalus cynoglossus and Atlantic cod Gadus morhua) of north-west Atlantic demersal fishes, corresponding to nine populations, were examined in relation to fish size and condition in an attempt to explain the observed variability in potential fecundity. Both relative body (K(r)) and liver condition (H(r)) were poor single-factor predictors of fecundity, and in almost all cases fish body mass (M) was the best single-factor predictor. Annual variability in mean K(r) and H(r) existed for most populations. The inclusion of K(r) and H(r) in total length (L(T))-based predictive models improved model fit only slightly and not significantly in all cases. Multiple regression analyses to determine the best model for explaining the variability in fecundity often excluded K(r), H(r) and L(T) in favour of M. The amount of variability in fecundity that could be explained by the factors analysed here was species specific, with the highest proportion explained for H. platessoides and the lowest for L. ferruginea. The highly variable, and sometimes unpredictable, nature of north-west Atlantic groundfish fecundity suggests the need to continue collecting such reproductive data on an ongoing basis.
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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 | 0.000 | 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".