Impact of microbial profile integration on machine learning predictions of methane production: synergies and trade-offs with physicochemical parameters
Bibliographic record
Abstract
Microbial sequencing data were rarely integrated into the prediction of methane production using machine learning (ML) models because of high dimensionality and the lack of a systematic way to evaluate the change of insight gained from modelling with only physicochemical information. Here, key taxa were extracted with co-occurrence network analysis to reduce the dimension of the microbial profile. With 101 datasets with paired microbial and physiochemical features, integrating microbial features significantly enhanced accuracy for predicting methane production, increasing average R 2 from 0.73 to 0.79 and reducing mean absolute error from 8.7 to 8.0. Notably, integrating microbial features altered physicochemical feature impacts, shifting both their importance and directional effects. This underscores how microbial data refine mechanistic understanding and synergistically improve prediction accuracy, addressing a key gap left by models relying solely on physicochemical parameters. The work advocates for systematic microbial feature inclusion to advance methane production modelling with ML frameworks
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".