Validation of the use of doubly labeled water for estimating metabolic rate in the green turtle (<i>Chelonia mydas L.</i>): a word of caution
Bibliographic record
Abstract
Marine turtles often have extremely high water turnover accompanied by a low field metabolic rate (FMR), a combination that can contraindicate the use of doubly labelled water (DLW). Therefore, we conducted a validation study to assess the suitability of the DLW technique for determining FMR of marine turtles. Six green turtles (22.42+/-3.13 kg) were injected with DLW and placed in a tank of seawater with a respirometer for continuous monitoring of oxygen consumption (MR) over a 5-day period. Trials were conducted for turtles in both fed and fasted states. Respiratory exchange ratio (RER) was determined in a dry respirometer and used to calculate energy expenditure. For fed and fasted turtles, total body water (TBW) was 66.67+/-3.37% and 58.70+/-7.63% of body mass, and water flux rates were 9.57+/-1.33% and 6.14+/-0.65% TBW day(-1), respectively. Water turnover in fasted turtles was 36% lower than that of fed turtles but MR (from oxygen consumption) of fasted turtles (13.77+/-1.49 kJ kg(-1) day(-1)) was 52% lower than in fed turtles (28.66+/-5.31 kJ kg(-1) day(-1)). Deuterium to oxygen-18 turnover rate (k(d):k(o)) ratios averaged 0.91+/-0.02 for fed turtles and 1.07+/-0.16 for fasted turtles. Fed turtles had a mean group difference of 8% and a mean individual difference of 53% between DLW and respirometry. The DLW method gave negative MR values in fasted turtles and could not be compared with respirometry data. Researchers should use caution when applying the DLW method in marine reptiles, especially when high water flux causes >90% of the labeled oxygen turnover to be due to water exchange.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".