Does shelter influence the metabolic traits of a teleost fish?
Bibliographic record
Abstract
Availability of shelter is an important component of habitat selection for animals as it can influence survival (protection against harsh physical conditions and predation) and growth (energy acquisition and expenditure). Few studies address the effect of shelter on metabolic expenditures associated with non-mechanical tasks (excluding station holding or movement). The main goal of this study was to investigate the influence of shelter use on metabolic traits of smallmouth bass (Micropterus dolomieu) from two populations (Kiamika River and Lake Long). Respirometry experiments on smallmouth bass were conducted to measure standard metabolic rate (SMR), resting metabolic rate (RMR), aerobic scope (AS), recovery time (RT) and excess post-exercise oxygen consumption (EPOC) in the presence or absence of shelter. The presence of shelter did not affect most metabolic traits, except for RMR, which was reduced in the presence of shelter for Lake Long fish. The results of this study also show that larger fish had lower SMR in the presence of shelter than when it was absent. When accounting for social hierarchy, there were no differences in most metabolic traits in dominant or subordinate fish in the presence or absence of shelter, except for RT, which was significantly lower in the presence of shelter for dominant fish. These results do not support the existence of an unequivocal relationship between individual metabolic traits and the presence of shelter. If physiological motives may influence the use of shelter, sheltering in itself might not have important consequences on energy expenditures required for non-mechanical tasks.
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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) | 0.002 | 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".