Stock and parental effects on embryonic and early larval development of winter flounder <i>Pseudopleuronectes americanus</i> (Walbaum)
Bibliographic record
Abstract
A hierarchical breeding design was used to determine if winter flounder Pseudopleuronectes americanus embryos and yolk‐sac larvae sired by Georges Bank males developed and grew larger than fish sired by Passamaquoddy Bay males, and to examine parental contributions to variations in fertilization success, time to 50% hatch, hatch success and larval morphological development. Significant stock effects were detected for time to hatch and larval development. Eggs fertilized by Passamaquoddy Bay males reached 50% hatch significantly earlier than eggs fertilized by Georges Bank males. Larvae sired by Georges Bank males were significantly larger during larval development for four of the six traits measured at 12 days post‐hatch: head depth, jaw length, myotome height and body area. Embryo and larval development were strongly influenced by maternal contributions; there were significant maternal variance components for the majority of the variables measured. Paternal variance components were significant for fertilization success, time to hatch, larval jaw length and larval head depth, however, they acted principally through parental interactions. This information has important implications for the long‐term sustainable development of winter flounder for aquaculture purposes as well as for understanding winter flounder genetic variation in the wild.
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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".