Spat production of the great scallop (<i>Pecten maximus</i>): a roller coaster<sup>1</sup>This review is part of a virtual symposium on current topics in aquaculture of marine fish and shellfish.
Bibliographic record
Abstract
The great scallop ( Pecten maximus (L., 1758)) has been of interest for aquaculture in Europe since the early 1970s. Since then, a large part of the research and development has focussed on reproduction and early life stages to support hatchery production of spat. Results from the last two decades show that production stability is lacking and have followed a roller-coaster trend. Production strategy varies, but in general, broodstock are collected from the wild and conditioned to gonad maturity sufficient for successful spawning. Natural reproduction cycle varies between populations, which is a challenge to hatcheries aiming at stable year-round production. Larval survival was for many years dependent on addition of antibiotics until a flow-through culture was established, and seasonal variation may be caused by variation in gamete or seawater quality. Settlement, metamorphosis, and spat growth depend on healthy larvae and appropriate culture environment. For efficient spat production, the use of land-based nurseries is promising. Results show that mean yield of spat from eggs is less than 1%. The review concludes that the gap between results obtained in hatchery production and in experiments shows a great potential for production increase.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".