Assessing the methane-mitigating effects of feed additives on dairy cows: Validation of an AI-Based predictive model using a monensin feed additive
Bibliographic record
Abstract
In response to the growing demand for sustainable agriculture and process optimization, our study introduces an innovative approach that meets these needs; utilizing rumen microbiome samples from Israeli Holstein cows across 14 commercial dairy farms, we constructed an AI-driven model that predicts the effect of feed additive on enteric methane emissions. The model extracts patterns from microbiome datasets using a network-oriented approach to process raw sequencing data and identifies statistically significant DNA patterns. The identified patterns serve as biomarkers to confirm their significant correlation with the efficacy of the feed additive. For the model validation, cows were given a monensin feed additive; in addition, enteric methane emissions were obtained before and during the 12 weeks of the trial, then in each farm the methane measurements were compared with the respective control group to estimate the accuracy of the AI-model predictions. The validation was performed on independent cohorts to ensure robustness. The results obtained indicated a high accuracy in the model predictions, achieving an average reduction of 20% on enteric methane emissions across 14 dairy farms, with peaks of 30% of reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".