Of mice and their environments. (Letter to the Editor).
Bibliographic record
Abstract
John Bohannon’s article "Can a mouse be standardized?" (News Focus, 20 Dec., p. 2321) cites differences in data between our laboratories in support of his argument that early rearing environments, especially caging and housing conditions, are important for results of behavioral tests of mice. However, his claim that "results varied wildly between labs" is inaccurate. We presented data for eight genetic strains of mice on five behavioral tests in three labs (1). For two tests, ethanol preference and water escape learning, the three labs obtained essentially the same results. For open field activity and cocaine activation, data for five of the strains were very similar in the three labs, whereas we obtained quite different results for three genetic groups derived from the 129 strain. Only on a test of anxiety was the variation among labs close to the magnitude of genetic variation; mice tested in Edmonton were generally less anxious than those tested in Portland. This very real environmental effect had nothing to do with early housing conditions; mice of a given swain were shipped from the same supplier on the same day to the three labs 6 weeks after birth and had identical environments before shipping.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.025 |
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".