SEROLOGIC SURVEY FOR POTENTIAL PATHOGENS AND ASSESSMENT OF DISEASE RISK IN AUSTRALIAN FUR SEALS
Bibliographic record
Abstract
The introduction of pathogens into populations of animals with no previous exposure to them and, therefore, no immunologic protection, can result in epizootics. Predicting the susceptibility of populations to infectious diseases is crucial for their conservation and management. Australian fur seals (Arctocephalus pusillus doriferus) have a relatively small population size, a restricted range, and form dense aggregations. These factors make this species vulnerable to epizootics of infectious diseases that spread by direct animal-to-animal contact. Blood samples were collected from 125 adult female Australian fur seals between 2007 and 2009 and tested for exposure to selected pathogens. The testing protocol was based on pathogens important to marine mammal health or those significant to public and livestock health. No antibodies were detected to morbilliviruses, influenza A viruses, six Leptospira serovars, Mycobacterium tuberculosis-complex species, or Toxoplasma gondii. Overall antibody prevalence to an unidentified Brucella sp. was 57% but varied significantly (P<0.02) between 2007 (74%) and 2008 (53%). The findings indicate Brucella infection may be enzootic in the Australian fur seal population. Further investigations are required to isolate the bacteria and establish if infection results in morbidity and mortality. Australian fur seals remain vulnerable to the threat of introduced disease and should be managed and monitored accordingly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".