Additional file 1 of Gene–gene interaction of AhRwith and within the Wntcascade affects susceptibility to lung cancer
Bibliographic record
Abstract
Additional file 1: Table S1. List of investigated SNPs (AhR/Wnt-markers and LC-markers). Table S2. Association of AhR/Wnt-marker within subgroups. Table S3. Association of markers reported elsewhere. Table S4. Score composition and importance ratio. Table S5. Discriminability of prediction scores. Table S6. Prediction accuracy of the decision trees. Table S7. Prediction accuracy in never smokers by histological subtypes. Table S8. Expression in normal tissue of the lung according LungGENS. Table S9. Expression in normal tissue of the lung according the Human Protein Atlas. Figure S1. Association of AhR/Wnt-markers within never and ever smokers. Figure S2. Decision tree for overall LC. Figure S3. LC-risk score: model selection and ROCs for overall LC. Figure S4. LC-risk score: model selection and ROCs for SCLC and Never smoker. Figure S5. Decision tree for early onset LC (age ≤55 years). Figure S6. Decision tree for SqCLC. Figure S7. Decision tree for SCLC. Figure S8. Decision tree for ever smoker. Figure S9. Expression profiles according to the Human Protein Atlas.
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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 | 0.001 | 0.026 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.874 | 0.152 |
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".