A field-based procedure for determining number of waste sorts for solid waste characterization
Bibliographic record
Abstract
Current methodologies for estimating the number of waste sorts require prior information on statistics (mean and standard deviation) for specific waste categories. The methods often require an iterative procedure or computer software to estimate the number of sorts required and the required statistics may change both spatially and temporally. To overcome the need for prior statistical information and avoid the need for iterative effort, a real-time methodology is developed to determine the required number of waste sorts for a waste category while sampling in the field for solid waste characterization. The information on required numbers of waste sorts is field-based and evolves during the sampling by utilizing real-time data to characterize the means and standard deviations. The proposed approach does not require prior information. The application of proposed methodology is demonstrated using the data from the Greater Vancouver Regional District (GVRD) for the primary categories of paper, glass, and secondary category glass-beverage.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".