Increased dairy farm methane concentrations linked to anaerobic digester in a five‐year study
Bibliographic record
Abstract
Abstract Organic waste materials are sources of anthropogenic methane (CH4) emissions. Anaerobic digestion (AD) is a technology that produces biogas from organic waste materials, and CH4 is the primary component of biogas. Unintended emission of CH4 from biogas facilities could undercut the environmental benefits of this technology. The objective of this study was to determine if the implementation of an AD system affected ambient CH4 concentrations ([CH4]) on a commercial dairy farm over 5 yr, from before installation into full operation. Concentrations at 4.5‐m height on a tower receiving wind that originated from various directions, comprising components of the dairy farm such as the AD facility, crop fields, or main barn, were measured using a closed‐path tunable diode laser trace‐gas analyzer. In 2012 and 2013, the first 2 yr of AD operation, [CH4] was not significantly different than pre‐AD levels in 2011 (2.04 ± 0.01 μl L−1). However, mean [CH4] increased to 2.47 ± 0.03 and 2.48 ± 0.04 μl L−1 in 2014 and 2015, respectively, and the occurrence of high [CH4] (>10 μl L−1) increased from <0.05% in Year 1 (pre‐AD) to 12% in Years 4 and 5. These elevated concentrations were related to an increased use of food waste feedstocks over time and suggest that the biogas system was a source of fugitive CH4 emissions. Food waste materials have a high biogas potential and are a valuable resource that require appropriate facility design and management to fully harness their benefits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".