Evolutionary individual-based model for the recruitment of anchovy (<i>Engraulis capensis</i>) in the southern Benguela
Bibliographic record
Abstract
Evolutionary simulations are developed to explore environmental constraints that select observed spatial and temporal spawning patterns for anchovy (Engraulis capensis) in the southern Benguela. They couple a realistic three-dimensional hydrodynamic model with an individual-based model in which an evolutionary-based reproductive strategy for adult fish and a passive transport for early life stages are implemented. The evolutionary success of spawning is quantified when patterns at the population level emerge after many generations from constraints at the individual level through a selective process. As a result, several self-sustaining populations are identified considering different sets of selective constraints. Simulated spawning patterns better match the observed mean spawning pattern when two selective environmental constraints are associated: a threshold temperature of 14°C, above which the development of early life stages is ensured, and the avoidance of offshore currents that constitute a loss of spawning products. Simulated recruitment patterns are more realistic when considering the constraint of reaching the nursery area. This modeling experience can help to identify, temporally and spatially, environmental factors important for fish recruitment and to establish a hierarchy of these factors. The probable coexistence in nature of several self-sustaining populations of pelagic fishes is shown to be important for recruitment studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".