Estimating natural interstage egg mortality of Atlantic mackerel (<i>Scomber scombrus</i>) and horse mackerel (<i>Trachurus trachurus</i>) in the Northeast Atlantic using a stochastic model
Bibliographic record
Abstract
Egg mortality is a key parameter for understanding early life histories of fish. Small variations in estimated mortality cause large differences on adult fish biomass estimates. Therefore, the assumption of a constant egg mortality rate may be misleading. Here, we show how to estimate mortality rates for the individual egg stages of Atlantic mackerel (Scomber scombrus) and horse mackerel (Trachurus trachurus) from triennial surveys conducted since 1977. We use a standard, continuous-time Markov process model that combines the numbers of eggs sampled in each stage with experimental data on egg stage duration (dependent on water temperature). This is the first attempt to study mortality among egg stages in such detail and the first comprehensive effort to estimate horse mackerel egg mortality in the Northeast Atlantic. The results include detailed descriptions of spatial–temporal dependencies in mortality. The daily egg mortality rates estimated are ~0.56·day–1 for Atlantic mackerel (far higher than suggested in the literature) and 0.54·day–1 for horse mackerel. Although it was not possible to estimate stage 1 egg mortality directly, the results suggest high mortality in the first stage. This might lead to underestimation of fish biomass when assessed traditionally by egg survey data alone.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".