Modeling the timing of spawning and hatching of shortnose sturgeon, <i>Acipenser brevirostrum</i>, in the Saint John River, New Brunswick, Canada
Bibliographic record
Abstract
Timing of spawning and hatching of shortnose sturgeon, Acipenser brevirostrum , in the Saint John River, New Brunswick, Canada, was estimated using inverse prediction. We examined egg incubation periods at 5, 9, and 13 °C to back-calculate spawning dates. No larvae hatched at 5 °C. At 9 and 13 °C, hatching began after 18 and 8 days post fertilization, respectively. Lengths of yolk-sac larvae reared in the laboratory at 13–21 °C were used to develop a temperature-mediated Gompertz growth model. The inverted Gompertz model, predicting larval age from larval size and water temperature, was applied to 671, 164, and 746 larvae captured in the wild in 2008, 2009, and 2010, respectively. Estimated hatching distributions peaked in late May, and mean spawning events were predicted to occur in late April – early May (9 °C scenario) and middle to late May (13 °C scenario). Larval ages at the two sampling transects, 4.5 km apart, were similar, while catch per unit effort was lower downstream, indicating mortality during dispersal. Inverse prediction of larval ages provides fast and cost-effective estimates of the timing of spawning, hatching, and larval migration 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".