Bibliographic record
Abstract
P.David Josephy mutagens have not yet been identified, nor have associations with breast cancer or environmental exposures been tested.University of Guelph, College of Physical and Engineering Science, However, the authors suspected that the mutagens are aromaticDepartment of Chemistry and Biochemistry, Guelph, Ontario, amines and stated that their study ‘lends support to theCanada N1G 2W1 hypothesis that genetic damage is an important mechanism for Dear Sir human mammary carcinogenesis’. In a recent issue of the journal, Feigelson and Henderson have In the search for the environmental causes of breast cancer presented a thorough review of the relationship between (14), we should not overlook the possible importance of estrogen exposure and breast cancer (1). Certainly, many of genotoxic carcinogens, as well as hormonal factors. the know risk factors for breast cancer, such as early menarche and hormone replacement therapy, can be explained on the References basis of hormonal effects. However, the authors state that ‘all 1.Feigelson,H.S. and Henderson,B.E. (1996) Estrogens and breast cancer.[emphasis added] of these [risk factors and protective factors] Carcinogenesis, 17, 2279–2284. can be understood as measures of the cumulative exposure of 2.Wolff,M.S., Collman,G.W., Barrett,J.C. and Huff,J. (1996) Breast cancer and environmental risk factors: epidemiological and experimental findings.the breast to estrogen and, perhaps progesterone’. This assertion Annu. Rev. Pharmacol. Toxicol., 36, 573–596.could be interpreted as ruling out a role for genotoxic chemical
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.051 | 0.032 |
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".