Modelling the dispersal and mortality of Browns Bank egg and larval haddock (<i>Melanogrammus aeglefinus</i>)
Bibliographic record
Abstract
An early life stage model is described with constant mortality for egg and larval stages. The model is used to simulate the stage 4 egg and larval data for haddock (Melanogrammus aeglefinus) in southwest Nova Scotia for the years 1983-1985. The model is initialized using published abundance and mortality estimates for these years, and its output is compared with field data. We find that the model does a systematically poor job of reproducing both the spatial-temporal and area-integrated versions of the data. To understand the discrepancy, we derive an integrated version of the model (integral model) and analyze its properties. This leads to a general method for determining whether sequential stage abundance data is consistent with a stage-dependent constant-mortality model. We use this method to show that a constant-mortality early life stage model is not consistent with the data. The integral model allows for year-day dependent mortality functions, which results in almost perfect fits to the abundance data. These functions can be transferred to the early life stage model with significantly improved model performance, although spatial differences remain. The implications of the integral-model analysis for sequential stage mortality estimation are discussed.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".