Lactate kinetics of rainbow trout during graded exercise: Do catheters affect the cost of transport?
Bibliographic record
Abstract
Changes in lactate kinetics as a function of exercise intensity have never been measured in an ectotherm. Continuous infusion of a tracer is necessary to quantify rates of lactate appearance (Ra) and disposal (Rd), but it requires double catheterization, which could interfere with swimming. Using rainbow trout, our goals were to: (1) determine the potential effects of catheters and blood sampling on metabolic rate (O2), total cost of transport (TCOT), net cost of transport (NCOT) and critical swimming speed (Ucrit), and (2) monitor changes in lactate fluxes during prolonged, steady-state swimming or graded swimming from rest to Ucrit. This athletic species maintains high baseline lactate fluxes of 24 μmol kg(-1) min(-1) that are only increased at intensities >2.4 body lengths (BL) s(-1) or 85% Ucrit. As the fish reaches Ucrit, Ra is more strongly stimulated (+67% to 40.4 μmol kg(-1) min(-1)) than Rd (+41% to 34.7 μmol kg(-1) min(-1)), causing a fourfold increase in blood lactate concentration. Without this stimulation of Rd during intense swimming, lactate accumulation would double. By contrast, steady-state exercise at 1.7 BL s(-1) increases lactate fluxes to ~30 μmol kg(-1) min(-1), with a trivial mismatch between Ra and Rd that only affects blood concentration minimally. Results also show that the catheterizations and blood sampling needed to measure metabolite kinetics in exercising fish have no significant impact on O2 or TCOT. However, these experimental procedures affect locomotion energetics by increasing NCOT at high speeds and by decreasing Ucrit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".