Additional file 1 of Persistent white matter vulnerability in a mouse model of mild traumatic brain injury
Bibliographic record
Abstract
Additional file 1: Table S1. Sample sizes for all experimental groups. A small subset of double-sham-receiving mice were included for histological analysis to serve as negative controls. Some mice receiving one or two mTBIs were excluded from histological analysis due to tissue damage. Table S2. Summary statistics and two-tailed t-test p-values of comparisons between mice receiving 1 or 2 mTBIs against mice receiving sham operations on the spontaneous alternations index and maximum number of alternations (total arm entrances—2) of the Y-maze test and the proportion of time spent on the safe side of the visual cliff test. Table S3. Summary statistics and one-tailed t-test p-values of comparisons of silver staining area in the corpus callosum or optic tract regions between mice receiving 1 mTBI and mice receiving 2 mTBIs. * = p< 0.05, ** = p< 0.01. Table S4. Output of multiple linear regression examining the effects of sex and impact severity on white matter damage in the corpus callosum. * = p< 0.05, ** = p< 0.01, *** = p< 0.001. Table S5. Output of multiple linear regression examining the effects of sex and impact severity on white matter damage in the optic tract. * = p< 0.05, ** = p< 0.01, *** = p< 0.001.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.806 | 0.117 |
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".