Analysis on the Influencing Factors of Transformation of Green Logistics Industry of Dangshan Pear Based on ISM
Bibliographic record
Abstract
The state strongly advocates the construction of ecological civilization, which brings opportunities to the production and consumption of green products. The green stream of the development of the pear (sweet pear) is of great significance for the county in Suzhou city, Anhui province, which is a major economic crop. There are many disadvantages in the traditional pear logistics mode, and it is changing in the direction of green logistics. In this paper, the author USES the interpretation structure model (ISM) method to refine and analyze the factors affecting the development of green logistics of the pear, and summarizes the 12 influencing factors and their interrelationships through literature collection, data access and other methods, and establishes the Adjacency matrix and the Accessibility matrix, and constructs the Six-order interpretation structure model. Based on the analysis of the model, six suggestions are proposed to promote the development of green logistics transformation in Dangshan County, and provide references for the development of green agricultural logistics industry in Dangshan County and other areas in China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".