Assessing supernumerary root occurrence as a possible adaptation enhancing teeth performance in Mediterranean deer populations
Bibliographic record
Abstract
Teeth root morphology and integrity are essential to provide appropriate attachment, allowing for continuous and functional movement, with implications for adequate food processing, animal performance, and longevity. We studied the occurrence of supernumerary roots in mandibular molariform teeth of red deer ( Cervus elaphus Linnaeus, 1758) from seven separate populations spanning a range of latitudes in the Iberian Peninsula. We analyzed the influence of several factors, including sex, origin (native vs. reintroduced), lineage, and habitat, to assess extra root prevalence variations. The highest prevalence in deciduous teeth was found in pm3 (14%) and in permanent teeth in M1 (3%). We found significant differences between areas, lineages, and soil type; however, no significant relationships were found with the origin or the sex of individuals. We speculate that the high prevalence of supernumerary roots in M1 might be related to increased wear in grazers. Furthermore, we suggest that this high prevalence in deciduous teeth might be associated with a hard diet, dry climatic conditions, and a harsh weaning period, which indicate that supernumerary roots might have an adaptive value.
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 | 0.001 | 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) | 0.001 | 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".