Effects of condition on fecundity and total egg production of eastern Scotian Shelf haddock (<i>Melanogrammus aeglefinus</i>)
Bibliographic record
Abstract
A fecundity study of the eastern Scotian Shelf haddock (Melanogrammus aeglefinus) stock during the 19971999 spawning seasons is reported. We developed a model that accounted for fecundity changes at the individual level and that could be used to estimate population egg production beyond the study period. Incorporating condition factors into the model (relative condition factor (Kn) and hepatosomatic index) accounted for a significant proportion of the residual variation. The model predicted that a change in Kn from 0.8 to 1.0 resulted in a twofold increase in fecundity at length. This variability was as great as that observed for fecunditylength relationships among stocks. Three time series (19792001) of total egg production (TEP) were constructed using different fecundity relationships: one with a condition effect, one based on length only, and one based on weight only. The magnitude of change in TEP resulting from the condition effect ranged from +30 to 20%. Condition effects during the first half of the time series resulted in an enhancement of TEP, whereas in the latter half, condition effects depressed TEP. This evaluation of TEP generated new insights into haddock stock dynamics but did not result in a dramatic improvement of the relationship between recruitment and stock reproductive potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".