Additional file 1 of TAOK3 limits age-associated inflammation by negatively modulating macrophage differentiation and their production of TNFα
Bibliographic record
Abstract
Additional file 1: Sup. Fig. 1. Additional data pertaining to Fig. 1. a) Dot plot indicating the mean weight of male and female mice in grams (g) in different age groups (young, adult, old). b) Bar chart indicating the mean age at humane endpoint between male and female Taok3-/- mice (top). Bar chart indicating the proportion of each sex within the dead animal group. c) Pathology report for brain, lung, liver, spleen, lymph node, and colon tissues fixed in formalin and analyzed with H&E staining. d) Micrograph of H&E-stained section from WT (left) and KO (right) axillary lymph nodes at 40X (top) and 100X (bottom) magnifications. e) n/nx plots for the parameters used to calculate the UMAP in Fig. 1g. Right side table represents the representative markers used to define major splenic populations. Data representative of at least three independent litters. Each animal represents one data point (a-b). Statistical analysis: a) Two-way ANOVA with Dunnet’s multiple comparisons. (b) unpaired, bilateral T-tests. Chart error bars represent mean±SEM. Non-significant differences, ns. *p≤0.05, **p≤0.01, ***p≤0.001, ****p≤0.0001.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.914 | 0.248 |
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".