Inferring odontocete life history traits in dentine using a multiproxy approach (δ <sup>15</sup> N, δ <sup>44/42</sup> Ca and trace elements)
Bibliographic record
Abstract
Rationale Understanding the interactions between marine mammals and their environment is critical for ecological and conservation purposes. Odontocetes offer a continuous record of their life history from birth as recorded in annual increments of their tooth dentine. Because dentine is not remodeled and contains collagen, nitrogen stable isotope compositions (δ 15 N) reflect nursing and weaning events, life history traits that would otherwise be impossible to retrieve in such elusive marine animals. Yet, capturing the magnitude and temporal changes in these events is constrained by tooth size and sampling resolution. Moreover, historical and fossil specimens undergo collagen decay, hence the need to develop the measurements of other proxies. Methods Here, we present a multiproxy approach to investigate the use of Ca isotope compositions (δ 44/42 Ca) in relation to δ 15 N and laser ablation profiles for different trace metal (Ba, Mg, Sr, Zn) concentrations across the dentine of a single individual of the common bottlenose dolphin Tursiops truncatus . Results To help interpret the dentine data, we provide milk elemental compositions and δ 44/42 Ca values for two odontocete individuals. We discuss the observed changes in δ 44/42 Ca across the dentine as potential markers of birth, weaning interval, incidental ingestion of seawater, trophic level and physiology. Incidental ingestion of seawater during nursing induces a positive offset in δ 44/42 Ca values recorded in the early formed dentine. Conclusions Life history parameters of individual marine mammals are extremely difficult to retrieve due to limitations in observing specimens in the wild and the methodology presented here offers new ecological and paleoecological perspectives.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".