Additional file 21 of Conventional laboratory housing increases morbidity and mortality in research rodents: results of a meta-analysis
Bibliographic record
Abstract
Additional file 21. Hypothetical data illustrating how ‘enrichments’ could impact external validity. This figure demonstrates how interactive effects between a treatment (e.g. a drug) and EH could impact experimental conclusions. Note that in each graph the error bars do not change, as EH does not change data variability. Also note that EH is not one unitary thing, but something that can vary in kind and degree (for example to deliberately introduce heterogeneity). (A) No interactive effect. No matter the cage condition, the experimental conclusion is the same: the drug reduces disease but does not cure it. EH does not affect external validity. (B) The drug effect is absent with EH. This drug could be useful for specific populations (e.g. chronically stressed and/or overweight subjects) but not others (e.g. physically fit content subjects). Testing the drug only under CH conditions will generate false positives, unless the target population is specifically one which is stressed and/or overweight etc. (C) The drug effect is only detectable in EH. This suggests the drug could be useful for some populations (e.g. ones which are physically fit and content) but not others (e.g. chronically stressed and/or overweight) Testing the drug only under CH conditions will generate false negatives, unless the target population is specifically one which is stressed and/or overweight.
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 | 0.006 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.795 | 0.044 |
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".