A method for evaluating and verifying biochemical methane potential test completion performed with landfilled municipal solid waste
Bibliographic record
Abstract
The biochemical methane potential (BMP) test is significant for the landfill industry as it provides a means to evaluate the gas potential, and therefore potential degradability, of both incoming and in-place municipal solid waste (MSW). However, the BMP test is not standardized making comparison of BMP results across sites problematic. For example, the BMP test duration has historically ranged from 20 days to several months with most current BMP tests lasting 60 days. However, the gas generation data can potentially be modelled for any of those durations to produce a prediction of the ultimate BMP value (BMP ULT ). Currently, the predicted BMP ULT values of 23 long-duration (115–150 days) BMP tests were used to determine the required quantity of data (i.e. number of days) needed to produce an accurate BMP ULT prediction. Results showed that no single test duration produced both accurate and efficient results, so a novel performance-based endpoint was proposed. The relative change in predicted BMP ULT values with respect to time (dBMP ULT /d t ) was chosen as a potential performance-based completion metric. Results indicate that once the absolute normalized dBMP ULT /d t value is within <2.5, <1.5 and <0.6% day −1 that the predicted BMP ULT is within 20, 10 and 5% of the true BMP ULT , respectively. Overall, the use of performance-based metrics for determining BMP test completion will allow for the collection of partial data sets, reduced experimental times and verification of results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".