SERUM CHEMISTRY AND ANTIBODIES AGAINST PATHOGENS IN ANTARCTIC FUR SEALS, WEDDELL SEALS, CRABEATER SEALS, AND ROSS SEALS
Bibliographic record
Abstract
Information on health parameters, such as antibody prevalences and serum chemistry that can reveal exposure to pathogens, disease, and abnormal physiologic conditions, is scarce for Antarctic seal species. Serum samples from Antarctic fur seals (Arctocephalus gazella, n=88) from Bouvetøya (2000-2001 and 2001-2002), and from Weddell seals (Leptonychotes weddellii, n=20), Ross seals (Ommatophoca rossii, n=20), and crabeater seals (Lobodon carcinophagus, n=9) from the pack-ice off Queen Maud Land, Antarctica (2001) were analyzed for enzyme activity, and concentrations of protein, metabolites, minerals, and cortisol. Adult Antarctic fur seal males had elevated levels of total protein (range 64-99 g/l) compared to adult females and pups (range 52-79 g/l). Antarctic fur seals had higher enzyme activities of creatine kinase, lactate dehydrogenase, and amylase, compared to Weddell, Ross, and crabeater seals. Antibodies against Brucella spp. were detected in Weddell seals (37%), Ross seals (5%), and crabeater seals (11%), but not in Antarctic fur seals. Antibodies against phocine herpesvirus 1 were detected in all species examined (Antarctic fur seals, 58%; Weddell seals, 100%; Ross seals, 15%; and crabeater seals, 44%). No antibodies against Trichinella spp., Toxoplasma, or phocine distemper virus (PDV) were detected (Antarctic fur seals were not tested for PDV antibodies). Antarctic seals are challenged by reduced sea ice and increasing temperatures due to climate change, and increased anthropogenic activity can introduce new pathogens to these vulnerable ecosystems and represent a threat for these animals. Our data provide a baseline for future monitoring of health parameters of these Antarctic seal species, for tracking the impact of environmental, climatic, and anthropogenic changes in Antarctica over time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".