Bibliographic record
Abstract
The ability to account with precision for the quantitative variation in the basal rate of metabolism (BMR) at the species level is explored in four groups of endotherms: arvicoline rodents, ducks, melaphagid honeyeaters, and phyllostomid bats. An effective analysis requires the inclusion of the factors that distinguish species and their responses to the conditions they encounter in the environment. These factors are implemented by changes in body composition and are responsible for the non-conformity of species to a scaling curve. Two concerns may limit an analysis. The factors correlated with energy expenditure often correlate with each other, which usually prevents them from being included together in an analysis, thereby preventing a complete analysis, implying the presence of factors other than mass. Many of the relevant factors, such as food habits and an island residence, are qualitative, which complicates their inclusion in a quantitative analysis, a difficulty that is solved by ANCOVA. The precision of an analysis, based on an inclusive equation, can be determined by comparing its estimates with measurements of the performance of species. Without this comparison, the effectiveness of an analysis cannot be determined, which then simply becomes a suggestion. A proposed standard for a precise estimate is for it to be within 10% of the measured rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".