Identification and ranking effective factors on establishment of green supply chain management in railway industry
Bibliographic record
Abstract
Globalization, intensification of governmental and non-governmental organization's provisions and squeeze and the demand of clients about concerning environmental affairs have motivated many organizations to start considering Green Supply Chain management in order to facilitate environmental and economic functions. Administration of green supply chain management unifies the management of supply chain with environment requirements during the functioning of levels of supply chain. This paper aims to identify green supply change management's factors in order to facilitate implementation of green supply chain management in rail industry. Thus, the effective factors of green supply chain management establishment are extracted and the method of interactions between sub-scales is studied by DEMATEL technique. The result specifies that reduction of production loss sub-scale is the most effective factor on other agents. In addition, the effective elements peered out by fuzzy TOPSIS that the scale of loss management stands on the first ranking and improvement of production process and interior environment management scales stand on the second and the third level and other scales follow them.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".