Bibliographic record
Abstract
如何用数字化手段打造一个高效且低成本的循环经济平台？万物新生案例回答了这个问题。万物新生的创业使命是“让闲置不用，都物尽其用”。它创建于2011年，从二手3C全场景回收平台爱回收起步，2017年打造了连接二手买家和卖家的B2B平台拍机堂，2019年合并了大股东京东旗下的二手交易平台拍拍，形成了“C2B+B2B+B2C”的闭环平台。在打造平台的过程中，万物新生将传统二手交易极具个性化的过程进行了标准化处理，制定了针对回收商品（手机）的质检工艺和流程标准、品质等级划分标准，打造了基于等级标准和其他大数据信息的C2B定价模型和B2B定价模型，并建立了“B2B定价+竞价”的交易机制，同时，还打造了“非标二手电子产品自动化输送、质检、分拣和存储”流水线，包括自动化或半自动化检测设备和立体仓库，布设了服务整个闭环的运营中心和供应链资源，这些动作旨在赋能平台合作伙伴并实现商品循环价值最大化。就在万物新生不断成长时，综合二手交易市场的头部企业开始发力3C回收市场。面对这样的竞争，万物新生要想持续做大，未来应该聚焦已有品类发展，还是应该积极开拓多品类发展？万物新生管理层正在讨论这个问题。
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".