A Comparative Study on the Typical Recycling Mode of Renewable Resources in China Under the Background of Internet
Bibliographic record
Abstract
China’s renewable resource recycling appeared such as 020, B2C, C2B and other new internet recovery mode. Through comparison of these three typical recovery modes, we found that, 020 mode provides door-to-door recovery service, so it can recycle all kinds of used materials. But the function of the network platform is single and this mode need large enterprises of renewable resources as the offline support; also, the door-to-door recovery methods lack of reasonable cost control mechanism, if these problems can be solved, this mode is suitable for all kinds of renewable resources recycling; B2C mode, with the most advanced reverse logistics tracing system, big date and cloud computing recycling technology, provides various services for all kinds of users, but the intelligent detection technology need a higher requirement of standardization level on renewable resources. Currently, this mode is only suitable for waste electrical and electronic products. With the improvement of intelligent detection technology, this mode has the potential for multi-category of renewable resources recycling; C2B mode, a typical bidding recovery mode, which can stimulate consumer to deliver their waste product actively through the network platform, but the bidding system exists loophole of malicious competition; also, this mode need a higher requirement of standardization level on the renewable resources, so this mode is only suitable for recycling of waste electrical and electronic products in urban areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".