A Fuzzy Composting Process Model
Bibliographic record
Abstract
Composting processes are normally complicated with a variety of uncertainties arising from incomplete or imprecise information obtained in real-world systems. Previously, there has been a lack of studies that focused on developing effective approaches to incorporate such uncertainties within composting process models. To fill this gap, a fuzzy composting process model (FCPM) for simulating composting process under uncertainty was developed. This model was mainly based on integration of a fractional fuzzy vertex method and a comprehensive composting model. Degrees of influence by projected uncertain factors were also examined. Two scenarios were investigated in applying the FCPM method. In the first scenario, model simulation under deterministic conditions was conducted. A pilot-scale experiment was provided for verifications. The result indicated that the proposed composting model could provide an excellent vehicle for demonstrating the complex interactions that occurred in the composting process. In the second scenario, application of the proposed FCPM was conducted under uncertainties. Six input parameters were considered to be of uncertain features that were reflected as fuzzy membership functions. The results indicated that the uncertainties projected in input parameters will result in significant derivations on system predictions; the proposed FCPM can generate satisfactory system outputs, with less computational efforts being required. Analyses on degree of influence of system inputs were also provided to describe the impacts of uncertainties on system responses. Thus, suitable measures can be adopted either to reduce system uncertainty by well-directed reduction of uncertainties of those high-influencing parameters or to reduce the computational requirement by neglecting those negligible factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".