Using the electron transport system as an indicator of organismal thermal tolerance and respiratory exploitation
Bibliographic record
Abstract
Freshwater ecosystems are undergoing rapid thermal shifts, making it increasingly important to understand species-specific responses to these changes. Traditional techniques for determining a species’ thermal tolerance are often lethal and time consuming. Using the enzyme activity associated with the electron transport system (ETS; hereafter referred to as enzyme assay) may provide a non-lethal, rapid, and efficient alternative to traditional techniques for some species. We used largemouth bass Micropterus salmoides (Lacepede, 1802) to test the efficacy of using an enzyme assay to determine thermal tolerance and respiratory exploitation in response to variable acclimation temperatures. Three tissue types were dissected from fish acclimated to 20, 25, or 30 °C and used in ETS assays at temperatures ranging from 7.5 to 40 °C. While there were significant differences among tissue types and acclimation temperatures, maximal enzyme activity occurred from 25.23 to 31.91 °C. Fish lost equilibrium at 39–42 °C in traditional CTmax trials, significantly higher than the upper optimum range determined via enzyme assays. The ratio of enzyme activity to measured whole organism respiration rate decreased with increasing water temperature, with the largest changes occurring at the upper optimum thermal range determined by enzyme assays. Our results indicate that ETS analysis may prove useful for obtaining biologically relevant thermal tolerances.
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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 | 0.001 | 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 | 0.001 |
| 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".