An experimental examination of temperature interactions in the match–mismatch hypothesis for Pacific cod larvae
Bibliographic record
Abstract
The match–mismatch hypothesis (MMH) predicts that marine fish larvae will have their highest rate of growth and survival when they overlap with their prey. However, Pacific cod ( Gadus macrocephalus ) and other gadids continue to thrive in the Bering Sea despite delayed prey production resulting from warming and loss of sea ice. In this study, we examined how temperature mediates growth and survival of Pacific cod larvae under varying match–mismatch conditions. Cod larvae were reared at two temperatures (3 °C and 8 °C) and exposed to one of five different food treatments over a 6-week period: (i) high food (HF) (match); (ii) low food (LF); (iii) HF then LF (HF–LF); and (iv) LF then HF (LF–HF) (2–4 mismatch); and (v) no food (NF) (complete mismatch). Results showed that cold environments allow Pacific cod larvae to bridge gaps in prey availability (i.e., timing and magnitude), but negatively impact survival over longer periods. Under warmer conditions, mismatches in prey significantly impacted growth and survival. However, both yolk reserves and compensatory growth mechanisms reduced the severity of mismatches occurring in the first 3 weeks of development. Our results demonstrate a clear need to incorporate the direct effects of temperature on fish larvae in food limitation models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".