Pilot study: decoding the skin microbiome of bowhead (<i>Balaena mysticetus</i>) and killer whales (<i>Orcinus orca</i>) in Nunavut, Canada
Bibliographic record
Abstract
Given the increasing challenges that Arctic cetaceans face, it is critical to investigate novel methods for assessing their health. Skin microbiomes have emerged as a promising method of detecting health issues that can help guide conservation efforts for free-ranging cetaceans. This study characterized the skin microbiome of 17 bowhead (BW) and 2 killer whales (KW). Fifty-six amplicon sequence variants were identified exclusively from cetacean samples, 20 belonged to BW, 13 to KW, and 23 to BW and KW. We identified bacteria from the genera Tenacibaculum and Psychrobacter, which have been previously described as bacteria that play a role in the health of cetaceans. In addition, in the healthy bowhead whale (H-BW) samples, we identified Clostridium sensu stricto 1 and 7, Carnobacterium spp., and Yersinia spp. which are of concern because these bacteria are opportunistic pathogens. Stranded BW had a less diverse microbiome than H-BW and had pathogens, including Aeromonas species and Streptococcus agalactia. Opportunistic pathogens of the genera Moritella (previously Vibrio spp.), Shewanella, Psychrilyobacter, and Legionella were discovered in KW. Due to their predator–prey relationships, the findings of this pilot study support the importance of keeping a close eye on the bowhead and killer whale populations in the Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".