A comparison between constitutive models for the municipal solid waste
Bibliographic record
Abstract
This paper compares the behavioural models of municipal solid waste (MSW) using the corresponding experimental data. To do so, the proposed models are first reviewed and, then, the algorithms and codes of different models are written. After obtaining each model's algorithm, the same experimental data are considered as input, and the strain-stress curve is plotted for each model. In the first method, the total strain in the waste is obtained based on the summation of the elastic, plastic, biological, and creep strains. Afterward, the equivalent stress is obtained. In this method, using biological changes over time, the age of the waste is calculated as an effective parameter in MSW behaviour. Moreover, the effect of creep on the waste is considered independently. In the second algorithm, MSW is considered as fibre and paste material, and the strain-stress curve is obtained. In this method, the waste is considered as a soil model, and the effect of different parameters are calculated. Due to the complexity of the MSW behaviour and considering various parameters, such as the age of the waste, E changes over time, creep, and biological changes, the Krase model has less error than the other models. Using the soil behaviour model for the waste has a significant error, indicating the difference between the results for the behaviours of the two substances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".