Influence of a dynamic rearing environment on development of metabolic phenotypes in age-0 Lake Sturgeon, Acipenser fulvescens
Bibliographic record
Abstract
Abstract Environment–phenotype interactions are the most pronounced during early life stages and can strongly influence metabolism and ultimately ecological fitness. In the present study, we examined the effect of temperature [ambient river temperature (ART) vs ART+2°C], dissolved oxygen (DO; 100% vs 80%) and substrate (presence vs absence) on standard metabolic rate, forced maximum metabolic rate and metabolic scope with Fulton’s condition factor (K), energy density (ED) and critical thermal maximum (CTmax) in age-0 Lake Sturgeon, Acipenser fulvescens, before and after a simulated overwintering event. We found that all the environmental variables strongly influenced survival, K, ED and CTmax. Fish reared in elevated temperature showed higher mortality and reduced K pre-winter at 127 days post-hatch (dph). Interestingly, we did not find any significant difference in terms of metabolic rate between treatments at both sampling points of pre- and post-winter. Long-term exposure to 80% DO reduced ED in Lake Sturgeon post-winter at 272 dph. Our data suggest that substrate should be removed at the onset of exogenous feeding to enhance the survival rate of age-0 Lake Sturgeon in the first year of life. Effects of early rearing environment during larval development on survival over winter are discussed with respect to successful recruitment of stock enhanced Lake Sturgeon, a species that is at risk throughout its natural range.
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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".