Distinct gut microbiomes in two polar bear subpopulations inhabiting different sea ice ecoregions
Bibliographic record
Abstract
Gut microbiomes were analyzed by 16S rRNA gene metabarcoding for polar bears (Ursus maritimus) from the southern Beaufort Sea (SB), where sea ice loss has led to increased use of land-based food resources by bears, and from East Greenland (EG), where persistent sea ice has allowed hunting of ice-associated prey nearly year-round. SB polar bears showed a higher number of total (940 vs. 742) and unique (387 vs. 189) amplicon sequence variants and higher inter-individual variation compared to EG polar bears. Gut microbiome composition differed significantly between the two subpopulations and among sex/age classes, likely driven by diet variation and ontogenetic shifts in the gut microbiome. Dietary tracer analysis using fatty acid signatures for SB polar bears showed that diet explained more intrapopulation variation in gut microbiome composition and diversity than other tested variables, i.e., sex/age class, body condition, and capture year. Substantial differences in the SB gut microbiome relative to EG polar bears, and associations between SB gut microbiome and diet, suggest that the shifting foraging habits of SB polar bears tied to sea ice loss may be altering their gut microbiome, with potential consequences for nutrition and physiology.
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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 | 0.001 | 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) | 0.000 | 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".