Validation of the doubly-labelled water technique in the domestic dog (<i>Canis familiaris</i>)
Bibliographic record
Abstract
We validated doubly-labelled water (DLW) by comparison to indirect calorimetry and food intake-mass balance in eight Labrador dogs (24-32 kg) over 4 d. We used several alternative equations for calculating CO2 production, based on the single- and two-pool models and used two alternative methods for evaluating the elimination constants: two-sample and multiple-sampling. In all cases the DLW technique overestimated the direct estimate of CO2 production. The greatest overestimates occurred with the single-pool model. Using two samples, rather than multiple samples, to derive the elimination constants produced slightly more discrepant results. Discrepancies greatly exceeded the measured analytical precision of the DLW estimates. The higher values with DLW probably occurred because the dogs were extremely active during the 1 h in each 24 spent outside the chamber. Estimates of CO2 production from food intake-mass balance, which include this activity, produced a much closer comparison to DLW (lowest mean discrepancy 0.3 % using the observed group mean dilution space ratio and an assumption that the mass changes reflected changes in hydration for all except one animal). We recommend an equilibration time of 6 h and use of the two-pool model based on the observed population dilution space for future studies of energy demands in dogs of this body mass.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".