Effects of warming winter embryo incubation temperatures on larval cisco ( <i>Coregonus artedi</i> ) survival, growth, and critical thermal maximum
Bibliographic record
Abstract
Abstract Freshwater whitefishes, Salmonidae Coregoninae, are cold stenothermic fishes of ecological and socio-economic importance in northern hemisphere lakes that are warming in response to climate change. To address the effect of warming waters on coregonine reproduction we experimentally evaluated different embryo incubation temperatures on post-hatching survival, growth, and critical thermal maximum of larval cisco ( Coregonus artedi ) sampled from lakes Superior and Ontario. Embryos were incubated at water temperatures of 2.0, 4.4, 6.9, and 8.9°C to simulate present and increased winter temperatures, and hatched larvae were reared in a common environment. For both populations, larval survival and critical thermal maximum were negatively related to incubation temperature, and larval growth was positively related to incubation temperature. The magnitude of change across incubation temperatures was greater in the population sampled from Lake Superior than Lake Ontario for all traits examined. The more rapid decrease in survival and critical thermal maximum across incubation temperatures for larval cisco in Lake Superior, compared to those from Lake Ontario, suggests that Lake Superior larvae may possess a more limited ability to acclimate to and cope with increasing winter water temperatures. However, the rapid increase in growth rates across incubation temperatures in Lake Superior larvae suggests they could recover better from hatching at a small length induced by warm winters, as compared to Lake Ontario larvae. Our results suggest propagation and restoration programs may want to consider integrating natural habitat preferences and maximizing phenotypic variability to ensure offspring are set up for success upon stocking.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".