The effects of 17β‐oestradiol, testosterone and tamoxifen on the development of papillomata in Catostomus commersoni
Bibliographic record
Abstract
Testosterone induced papillomata in 85% (11/13) of initially non‐papillomatous white suckers Catostomus commersoni and increased papillomata growth in 100% (16/16) of initially papillomatous suckers. 17β‐oestradiol induced papillomata in 83% (10/12) of initially nonpapillomatous suckers and increased papillomata growth in 100% (16/16) of initially papillomatous suckers. Less than 29% (4/14) of white suckers injected with tamoxifen developed papillomata, while complete papillomata regression was observed in 71% (10/14) of initially papillomatous suckers. In control groups 59% (27/46) of suckers either retained or developed papillomata and 27% (6/24) of suckers exhibited tumour regression. Protein kinase C (PKC) activity was significantly depressed and ornithine decarboxylase (ODC) activity was significantly elevated in steroid‐treated papillomata v. normal lip epidermis. ODC activity was significantly depressed in tamoxifen‐injected, regressing papillomata. There were no significant differences in plasma oestrogen and testosterone levels between papillomatous and nonpapillomatous female fish from a site contaminated with persistent organic chemicals and an uncontaminated reference site. Similarly, no significant differences in testosterone and 11‐ketotestosterone levels were observed between papillomatous and non‐papillomatous male fish.
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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 | 0.000 | 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) | 0.001 | 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".