Describing variability of MSW composition data with the log-logistic distribution
Bibliographic record
Abstract
Variations in solid waste composition data are necessary as inputs to solid waste planning, yet uncertainty exists regarding which probability distributions might be generally valuable to describe the variability. Twenty-two detailed analyses of solid waste from British Columbia, Canada, were fitted to distributions using the BestFit software. Alternative distributions were ranked based on three goodness-of-fit parameters and twelve waste fractions. The log-logistic distribution was found to be the most able to fit over the wide range of composition types considered. The results were demonstrated to be insensitive to the number of waste components or to the choice of a two- or three-parameter distribution. Although other distributions were able to better match the waste composition for individual waste types, the log-logistic distribution was demonstrated to fit, overall, a wide variety of waste composition types.
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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 | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".