Relating plaice (<i>Pleuronectes platessa</i>) recruitment to deteriorating habitat quality: effects of macroalgal blooms in coastal nursery grounds
Bibliographic record
Abstract
Concentration of juveniles of marine fishes in nurseries may act as a bottleneck during the life cycle, where quantity and quality of nurseries determine population size. Macroalgal blooms have become a common phenomenon in eutrophic shallow waters worldwide, and matforming algae may now cover many essential nursery habitats. In this investigation, the aim was to assess the quantitative effect of algal mats on the recruitment of plaice (Pleuronectes platessa) from nurseries in the Swedish Skagerrak archipelago. A model was constructed using data on nursery size, settling density, and mortality of plaice combined with data on algal distribution. Recruitment of 0-group plaice from nurseries could be reduced by 30%40% due to algae. The largest negative effect occurred during high settlement, reducing the important influence of strong year classes on stock size. The model predicted a reduction of juveniles due to algae of 4546 × 106 individuals at high settlement. This amounts to 68% of the output at medium settlement and equals the amount of plaice produced during 5 years of low settlement. Up to 75% of the total reduction could occur in one quarter of the study area. With limiting resources, management actions should not be generally applied but rather be concentrated to optimize the cost-benefit of measures taken.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".