A modelling study of Norway lobster (Nephrops norvegicus) larval dispersal in southern Portugal: predictions of larval wastage and self-recruitment in the Algarve stock
Bibliographic record
Abstract
A set of simulations using a validated and realistic parameterization of a numerical model was conducted for the south and southwest Portuguese regions as an attempt to understand larval dispersal patterns in the Norway lobster ( Nephrops norvegicus ). Larvae were introduced in the model as Lagrangian particles with five different behavioural scenarios concerning their ability to migrate vertically. Growth rate was temperature dependent and the larvae were tracked individually. The end point of the simulations was the position of the larvae when they reached competency at age 1. Age 1.25 was also considered to simulate a possible delay in settling due to lack of an appropriate substrate. The results showed that the majority of the larvae reached age 1 near the hatching area along the southern shelf, while low exchange of larvae between the south and the west coasts was observed, especially for behavioural scenarios where larvae remained in relatively shallow waters. Scenarios where larvae performed diurnal vertical migration and delayed settlement until age 1.25 indicated a tendency for westward motion because of interactions with the Mediterranean undercurrent. Self-recruitment to the Algarve stock was estimated at 0.2% to 0.5%, raising the concern that this stock may be experiencing recruitment limitation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".