Amino acid δ15N differences consistent with killer whale ecotypes in the Arctic and Northwest Atlantic
Bibliographic record
Abstract
Ecotypes are groups within a species with different ecological adaptations than their conspecifics. Eastern North Pacific (ENP) killer whale (Orcinus orca) ecotypes differ in their diet, behavior, and morphology, but the same is not known for this species in the eastern Canadian Arctic (ECA) and Northwest Atlantic (NWA). Using compound-specific stable isotope analysis (CSIA) of amino acids (AAs), we compared δ15N patterns of the primary trophic and source AA pair, glutamic acid/glutamine (Glx) and phenylalanine (Phe), in dentine collagen of (1) sympatric ENP killer whale ecotypes with well-characterized diet differences and (2) ECA/NWA killer whales with unknown diets. δ15NGlx-Phe was significantly higher in the ENP fish-eating (FE) than mammal-eating (ME) ecotype (19.2 ± 0.4‰ vs. 13.5 ± 0.7‰, respectively). Similar bimodal variation in δ15NGlx-Phe indicated analogous dietary divisions among ECA/NWA killer whales, with two killer whales having higher δ15NGlx-Phe (16.5 ± 0.0‰) than the others (13.5 ± 0.6‰). Inferences of dietary divisions between these killer whales were supported by parallel differences in threonine δ15N (-33.5 ± 1.6‰ and -40.4 ± 1.1‰, respectively), given the negative correlation between δ15NThr and TP across a range of marine consumers. CSIA-AA results for ECA/NWA whales, coupled with differences in tooth wear (a correlate for diet), are consistent with ecotype characteristics reported in ENP and other killer whale populations, thus adding to documented ecological divergence in this species worldwide.
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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 | 0.000 |
| 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".