Allometric respiration/body mass data for animals to be used for estimates of inhalation toxicity to young adult humans
Bibliographic record
Abstract
The relationship between body weight (BW) and respiratory minute volume (V(m)) was reviewed by collecting a database from the literature. The data were separated into anaesthetized and non-anaesthetized groups. Only young adult terrestrial mammals were included in the final data set. This database is the largest to be reported to date, is the first to separate the anaesthetized and non-anaesthetized groups and is matched to the target population of young, fit adult humans. The data set of non-anaesthetized animals contained 142 studies representing 2616 animals and 18 species from mice at 12 g body weight to horses and a giraffe at ca. 500 kg body weight. Analysis of the data indicated a power law (allometric) relationship between the minute volume and body weight. The resulting allometric equations for the empirical relationship between minute volume and body weight are: log(10)V()(m)= -0.302 + 0.809 log(10)BW and V(m) = 0.499 BW(0.809)where V(m) is the minute volume (l min(-1)) and BW is the body weight (kg). From these equations, a minute volume of 15.5 lmin(-1)was obtained for a 70 kg human in the same physiological and/or emotional state as the animals. The results of the analyses were compared to other empirical studies in the literature, the more recent of which also indicated a scaling factor of 0.8. The relationship between minute volume and body weight is recommended for use in estimating the inhalation toxicity to young adult humans (military personnel), because this is the first study to use a large database focused exclusively upon non-anaesthetized young adult terrestrial mammals.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".