Improving Inter-Laboratory Reproducibility in Measurement of Biochemical Methane Potential (BMP)
Bibliographic record
Abstract
Biochemical methane potential (BMP) tests used to determine the ultimate methane yield of organic substrates are not sufficiently standardized to ensure reproducibility among laboratories. In this contribution, a standardized BMP protocol was tested in a large inter-laboratory project, and results were used to quantify sources of variability and to refine validation criteria designed to improve BMP reproducibility. Three sets of BMP tests were carried out by more than thirty laboratories from fourteen countries, using multiple measurement methods, resulting in more than 400 BMP values. Four complex but homogenous substrates were tested, and additionally, microcrystalline cellulose was used as a positive control. Inter-laboratory variability in reported BMP values was moderate. Relative standard deviation among laboratories (RSDR) was 7.5 to 24%, but relative range (RR) was 31 to 130%. Systematic biases were associated with both laboratories and tests within laboratories. Substrate volatile solids (VS) measurement and inoculum origin did not make major contributions to variability, but errors in data processing or data entry were important. There was evidence of negative biases in manual manometric and manual volumetric measurement methods. Still, much of the observed variation in BMP values was not clearly related to any of these factors and is probably the result of particular practices that vary among laboratories or even technicians. Based on analysis of calculated BMP values, a set of recommendations was developed, considering measurement, data processing, validation, and reporting. Recommended validation criteria are: (i) test duration at least 1% net 3 d, (ii) relative standard deviation for cellulose BMP not higher than 6%, and (iii) mean cellulose BMP between 340 and 395 NmLCH4 gVS−1. Evidence from this large dataset shows that following the recommendations—in particular, application of validation criteria—can substantially improve reproducibility, with RSDR < 8% and RR < 25% for all substrates. The cellulose BMP criterion was particularly important. Results show that is possible to measure very similar BMP values with different measurement methods, but to meet the recommended validation criteria, some laboratories must make changes to their BMP methods. To help improve the practice of BMP measurement, a new website with detailed, up-to-date guidance on BMP measurement and data processing was established.
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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 | 0.161 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".