Bibliographic record
Abstract
There is a correlation between intestinal cancer and diets high in meat, so fried beef, chicken, lamb, pork and fish were tested for their ability to induce mutations in the small intestine of mice. The mice were bred to be heterozygous at the Dlb-1 locus so that loss of the dominant Dlb-1 b allele by mutation could be detected. Mice were fed the AIN-76A diet (which contains 50% of the calories in the form of sucrose) or an isocaloric diet in which the sucrose was replaced by meat or fish, for 5 or 9 weeks. Manifestation of mutants requires approximately 1 week in this system, so this corresponds to an effective exposure of 4 and 8 weeks, respectively. There was no significant difference in the weights of animals on the different diets, and no difference in mutant frequency. Several food mutagens were present, but at low levels. These results, when considered in the light of tests of 2-amino-1-methyl-6-phenylimidazo[4,5-b] pyridine and amino(alpha)carboline at much higher doses (Zhang,X.-B., Tao,K.S., Urlando,C., Shaver-Walker,P. and Heddle,J.A. (1996) MUTAGENESIS:, 11, 43-48), indicate that there is no highly mutagenic compound missed by previous testing with bacterial assays and that mixtures of heterocyclic amines at low levels do not show great synergy.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".