Neonatal line may develop after birth in the Indo-Pacific bottlenose dolphin (<i>Tursiops aduncus</i>)
Bibliographic record
Abstract
Studies using teeth to estimate age in marine mammals presume that the neonatal line (NNL) develops at birth. This study of Indo-Pacific bottlenose dolphins (Tursiops aduncus (Ehrenberg, 1833)) is the first to investigate when the NNL appears in odontocete dentine. Two to four teeth were prepared by decalcification, thin-sectioning, and staining for 103 dolphins, including 7 dolphins of known age. Tooth length, prenatal and postnatal dentine and NNL widths were measured. Developmental class (foetus, young neonate, older neonate, <1-year-old calf, 1-year-old calf) was assigned using carcass external features. NNL presence or absence was categorised for individual dolphins. The NNL was absent in a near-term foetus and all except one young neonate and fully formed in 50% of older neonates, whose estimated ages were 1 week to 2 months. It was absent in a known-age dolphin aged 4–7 weeks. NNL width was greater in dolphins less than 1 year old compared with those that were 1 year old. Factors that trigger NNL development are unknown. The present study suggests that the NNL may not be related to birth per se in dolphins, as has been clearly demonstrated in humans. Physiological processes, driven by diet, and behavioural changes during the first few months of postnatal life may be important drivers for NNL formation in odontocetes.
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 | 0.000 | 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) | 0.001 | 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".