EPIDEMIOLOGY OF NEOPLASIA IN CAPTIVE BLACK-FOOTED FERRETS (MUSTELA NIGRIPES), 1986-1996
Bibliographic record
Abstract
The epidemiology of neoplastic disease was studied retrospectively in the captive population of black-footed ferrets (Mustela nigripes). Postmortem reports were reviewed and archived tissues examined from 184 of the 227 adult (>1 yr old) black-footed ferrets that died from the beginning of the current captive propagation program in late 1985 to the end of 1996. A total of 185 neoplasms, of 28 distinct phenotypes, were seen in 102 (55.4%) of these ferrets. There was more than one tumor type present in 51 ferrets. Tumors of the apocrine glands (28.3%), renal tubular neoplasms (20.7%), and biliary cystadenoma or carcinoma (20.1%) were the most common neoplasms. The probability of developing most types of neoplasms increased with age. Neoplasms of the apocrine glands were more common in males and may be hormonally influenced. The unusually high prevalence of biliary cystadenocarcinoma may be secondary to the common occurrence of intrahepatic biliary cysts in this population. Although neoplasia is an important cause of mortality in captive adult black-footed ferrets, its impact on captive propagation of the species, and on the wild population, is probably limited because clinically significant tumors are encountered almost exclusively in postreproductive ferrets (>3 yr old) and because ferrets released into their natural habitat rarely reach susceptible age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".