Additional file 4 of Deep behavioural phenotyping of the Q175 Huntington disease mouse model: effects of age, sex, and weight
Bibliographic record
Abstract
Additional file 4: Figure S4. Normalized (weight-corrected) and raw data for water T-maze in male and female manifest zQ175dn mice compared to wild-type (WT) littermates. A) Time to platform normalized by weight during the acquisition phase (males, day 2 difference p = 0.0155, day 3 difference, p = 0.0097 (multiple comparisons); females, day 3 difference, p = 0.0132 (multiple comparisons). B) Average number of arm entries during the acquisition phase. C) Time to platform normalized by weight during the reversal phase. D) Average number of arm entries during the reversal phase. E) Time to platform (non-normalized) for male and female manifest zQ175dn in the water T-maze during the acquisition phase. F) Time to platform (non-normalized) for male and female manifest zQ175dn in the water T-maze during the reversal phase. Note: due to a flash drive error, 2 female manifest zQ175dn and 5 WT littermates were excluded from analysis for days 1 and 2 of acquisition for water T-maze experiments (therefore for these two days n = 15 for female manifest zQ175dn and n = 6 for WT littermates). See methods for details. Two-way analysis of variance [ANOVA] with multiple comparisons was used for all statistical analysis. Individual values for groups with n < 6 are provided in Additional file 7: Individual values. ns = not significant. M = male. F = female.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.826 | 0.136 |
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".