Patterns of trace element deposition in beluga whale teeth reflect early life history
Bibliographic record
Abstract
Determination of trace element concentrations in continuously growing biological structures such as otoliths, whiskers, and teeth can provide important insight into physiological and ontogenetic processes. We examined concentrations of 11 trace elements (Li, Mg, Mn, Cu, Zn, Se, Rb, Sr, Cs, Ba, Pb) in the annual dentine growth layer groups (GLGs) of teeth of 66 Eastern Canadian Arctic belugas (Delphinapterus leucas). Several of these trace elements displayed clear and consistent patterns in early life, though few longer term trends or signals were present in trace element data for either females or males. Large changes in Sr and Ba concentrations in fetal dentine reflected in utero shifts in element deposition in the teeth of developing belugas. Marked changes in these elements during the first years after birth were likely associated with the onset of nursing and subsequent weaning. Mg, Mn, and Zn also displayed clear and consistent patterns in early life that correlated strongly with dentine stable nitrogen isotope (δ15N) data, suggesting these elements merit further study as potential tools for studying nursing and weaning. Depositional patterns of Zn and Pb, which have been linked to sexual maturation in female Pacific walruses (Odobenus rosmarus divergens), were inconsistent in beluga teeth. Some individuals (including males) displayed patterns strongly resembling those observed in female walruses, whereas many animals did not, perhaps because they had not yet reached sexual maturity. The lack of clear patterns in trace element deposition after the first few years of life may have resulted from pooling samples from multiple populations/regions collected across more than two decades, but may also indicate that elemental concentrations are primarily driven by other, extrinsic processes later in life, and might be useful as biomonitors of environmental element concentrations or tools for delineating population structure.
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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 | 0.000 |
| 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".