Poor taxonomical knowledge of larval fish prey preference is impeding our ability to assess the existence of a “critical period” driving year-class strength
Bibliographic record
Abstract
Abstract Despite 100 years of research testing the link between prey availability during the larval stage and year-class strength, field-based evidence for Hjort's “critical period” hypothesis remains equivocal. Here, we argue that a minority of past studies have relied on sufficient taxonomical knowledge of larval fish prey preference to reveal the potential effects of variability in zooplankton prey production on larval vital rates and year-class strength. In contrast to the juvenile and adult stages, larval fish diet and prey field are often poorly resolved, resulting in the inclusion of zooplankton taxa that do not actually contribute to the diet as part of the prey field considered by fisheries scientists. Recent studies have demonstrated that when accounting for prey selectivity, the expected positive relationships between preferred prey availability and larval feeding success, growth and survival are revealed. We strongly recommend that laboratories conducting research on larval fish trophodynamics take prey selectivity into account and acquire the necessary taxonomic expertise for providing valid assessments of the influence of prey availability on larval vital rates. We make the prediction that the proportion of studies supporting the existence of a “critical period” will increase proportionally to the progress of knowledge on prey preference during the early larval stage.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".