Rumen–reticulum characteristics, scaling relationships, and ontogeny in white-tailed deer (<i>Odocoileus virginianus</i>)
Bibliographic record
Abstract
Scaling relationships between body mass and gut capacity are valuable to predicting digestive efficiency. Interspecific scaling relationships between body mass and gut capacity have consistently estimated a slope of 1.0; however, intraspecific scaling relationships between body mass and gut capacity have been highly variable. We examined the influence of demands of growth and production on scaling relationships of body mass and rumen–reticulum characteristics in white-tailed deer ( Odocoileus virginianus (Zimmermann, 1780)) because little is known about how juvenile and subadult ruminants accommodate increased digesta masses. We sampled 108 animals over a 2-year period and assessed the influence of body mass, time of kill, crude protein (%), and acid detergent fiber (%) in the rumen, lactation, sex, and back fat on rumen–reticulum organ mass, rumen–reticulum capacity, wet mass of the digesta, and the dry mass of the digesta. Juvenile and subadult white-tailed deer had rumen–reticulum organ masses, capacity, and digesta masses that were similar to adults because body mass and rumen–reticulum scaling relationships all had scalars similar to 1.0. Thus, under the confines of our study, ontogeny plays only a minor role in the physiological characteristics of the rumen–reticulum and the scaling relationships of body mass and rumen–reticulum capacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".