Combining <i>δ</i> <sup>13</sup> C and <i>δ</i> <sup>15</sup> N from bone and dentine in marine mammal palaeoecological research: insights from toothed whales
Bibliographic record
Abstract
Stable carbon (δ13C) and nitrogen (δ15N) isotopic compositions of bone and dentine collagen extracted from museum specimens have been widely used to study the paleoecology of past populations. Due to possible systematic differences in stable isotope values between bone and dentine, dentine values need to be transformed into bone-collagen equivalent using a correction factor to allow comparisons between the two collagen sources. Here, we provide correction factors to transform dentine δ13C and δ15N values into bone-collagen equivalent for two toothed whales: narwhal and beluga. We sampled bone and dentine from the skulls of 11 narwhals and 26 belugas. In narwhals, dentine was sampled from tusk and embedded tooth; in belugas, dentine was sampled from tooth. δ13C and δ15N were measured, and intra-individual bone and dentine isotopic compositions were used to calculate correction factors for each species. We detected differences in δ13C and δ15N. In both narwhals and belugas, we found lower average δ13C and δ15N in bone compared with dentine. The correction factors provided by the study enable the combined analysis of stable isotope data from bone and dentine in these species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".