Modeling Of Cod Eggs And Larvae Drift, Growth And Survival In The Gulf Of St. Lawrence, Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Connectivity of stocks through eggs and larvae drift might be an important parameter influencing the recruitment of cod. Unfortunately, in many cases, the scarcity of observations precludes a detailed analysis of the drift and its variability. To address the issue, we developed a three-dimensional biophysical modelling system to hindcast oceanic conditions and the drift, growth and survival of the early life stages of cod in the Southern Gulf of St. Lawrence and Northeast Scotian Shelf (Canada). Individual-Based Models (IBM) of cod eggs and larvae are incorporated into a full 3-D hydrodynamic model of the ocean. All the data required to drive the model from 1950 to 2003 have been assembled. Specific indices of the drift, growth and survival are developed for fishing areas. The results show that there is strong year-to-year variability in the drift and survival of the larvae. Comparisons with snow crab larvae simulations show that the settlement patterns of the two species are different due to the timing of the spawning, the vertical distribution of the larvae and larval stage lengths.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".