The interplay of fall and winter environments on overwintering potential in age-0 Pacific cod ( <i>Gadus macrocephalus</i> )
Bibliographic record
Abstract
Fish in higher latitudes must accumulate sufficient size and energy in fall to survive winters of low productivity. In this study, we measured the growth, condition, and survival of age-0 Pacific cod held in the laboratory under varying fall and winter food-temperature scenarios. Individually tagged fish were held for 6 weeks in the fall at either 7.0 or 10.0 °C under a “low” or “high” food ration (1.9%–2.6% vs. 4.2%–5.9% body weight d –1 , respectively) and redistributed into a series of new tanks to track survival and lipid loss in the absence of food across four winter temperatures (1.0, 2.5, 4.0, and 6.0 °C). Cooler winters and larger body size improved winter survival but were less important when fall conditions were unsuitable for lipid accumulation and growth, e.g., warm, low food scenarios. Lipid reserves explained overwintering winter survival, and survival outcomes were better predicted using environmental proxies and fall condition indices than simple size-based models. These results suggest that winter is a high mortality period, but the odds for winter survival are more likely predetermined by feeding conditions in the fall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".