Additional file 9 of Elucidating the role of the gut microbiota in the physiological effects of dietary fiber
Bibliographic record
Abstract
Additional file 9: Figure S3. Confirmation of gut microbiota compositional features and mechanistic biomarkers that predict clinical responses. Line graphs show differences in the effects of A AX on perceived satiety after a meal and HOMA-IR and B MCC on fecal calprotectin and TNF-α for high and low responders, as defined according to the study cohort median. AUC-ROC values show the performance accuracy of random forest classifiers for predicting high-vs-low responders in C AX-induced perceived satiety after a meal and HOMA-IR attenuation, D MCC-induced fecal calprotectin and TNF-α attenuation, and E AX and MCC induced changes in HOMA-IR, fecal calprotectin, and TNF-α. High and low responders were defined according to the study cohort median. Black cells denote OOB error rates ≥ 0.6. Prediction performance of random forest classifiers trained to predict high-vs-low responders in AX-induced F and G satiety after a meal and H HOMA-IR attenuation, and MCC-induced I fecal calprotectin and J TNF-α attenuation. OOB shows the mean prediction error of the random forests model with boosted decision trees (n = 500). The confusion matrix shows subgroup prediction accuracy, where row i and column j indicates the number of subjects predicted as i but were actually classified as j. Error rates indicate the percentage of incorrect classifications. ∆, absolute change from baseline to week 6; %∆, percent change from baseline to week 6; ASV, amplicon sequence variant; AX, arabinoxylan; AUC-ROC, area under the receiver operating characteristic curve; HOMA-IR, homeostatic model assessment of insulin resistance; MCC, microcrystalline cellulose; OTU, operational taxonomic unit; OOB: out-of-bag; SLIM, Satiety Labeled Intensity Magnitude; TNF-α, tumor necrosis factor-α.
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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 | 0.003 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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) | 0.918 | 0.179 |
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".