Additional file 1 of Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival
Bibliographic record
Abstract
Additional file 1: Table S1. Super-pathways represented by the 158 metabolites passing quality control and pre-processing filters. Table S2. Performance measures of machine learning algorithms trained under the precision recall (PR) curve in discriminating survival status using metabolomics data. Table S3. Variable distributions in complete imputed dataset and without imputation. Figure S1. A Total variance explained by each principal component and [middle and bottom panels] cumulative variance explained by each component (in pink) shown with the cross-validated variance explained (in blue). B Twenty principal components are required to explain 80% of the variance in the data. Figure S2. A Plot of the first 2 principal components. Ellipse captures 95% of the data. B Metabolites contributing to the largest loading weights for the first and second PC. Figure S3. Super pathways represented among top metabolites ranked by machine learning approaches. Figure S4. ROC curves for models in Table S2. Figure S5. Pairwise comparisons of normalized top metabolite levels, stratified by survival status. (Figure separated for data visualization purposes only.)
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.882 | 0.157 |
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".