Measurement of Whole-Body CO<sub>2</sub>Production in Birds Using Real-Time Laser-Derived Measurements of Hydrogen (δ<sup>2</sup>H) and Oxygen (δ<sup>18</sup>O) Isotope Concentrations in Water Vapor from Breath
Bibliographic record
Abstract
The doubly labeled water (DLW) method is commonly used to measure energy expenditure in free-living wildlife and humans. However, DLW studies involving animals typically require three blood samples, which can affect behavior and well-being. Moreover, measurement of H (δ(2)H) and O (δ(18)O) isotope concentrations in H2O derived from blood using conventional isotope ratio mass spectrometry is technically demanding, time-consuming, and often expensive. A novel technique that would avoid these constraints is the real-time measurement of δ(2)H and δ(18)O in the H2O vapor of exhaled breath using cavity ring-down (CRD) spectrometry, provided that δ(2)H and δ(18)O from body H2O and breath were well correlated. Here, we conducted a validation study with CRD spectrometry involving five zebra finches (Taeniopygia guttata), five brown-headed cowbirds (Molothrus ater), and five European starlings (Sturnus vulgaris), where we compared δ(2)H, δ(18)O, and rCO2 (rate of CO2 production) estimates from breath with those from blood. Isotope concentrations from blood were validated by comparing dilution-space estimates with measurements of total body water (TBW) obtained from quantitative magnetic resonance. Isotope dilution-space estimates from δ(2)H and δ(18)O values in the blood were similar to and strongly correlated with TBW measurements (R(2) = 0.99). The (2)H and (18)O (ppm) in breath and blood were also highly correlated (R(2) = 0.99 and 0.98, respectively); however, isotope concentrations in breath were always less enriched than those in blood and slightly higher than expected, given assumed fractionation values between blood and breath. Overall, rCO2 measurements from breath were strongly correlated with those from the blood (R(2) = 0.90). We suggest that this technique will find wide application in studies of animal and human energetics in the field and laboratory. We also provide suggestions for ways this technique could be further improved.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".