Bibliographic record
Abstract
本案例描述了蚂蚁森林的运营模式及取得的阶段性成果。蚂蚁森林不仅创造了环保公益新模式，还为支付宝及其母公司蚂蚁金服创造了独特的商业价值。第一，它搭建了一个人人可参与的平台，让“看得见的绿色”（种树）与“看不见的绿色”（低碳生活）构成相互激励闭环。第二，它用环保公益由头，吸引用户借助支付宝实现低碳应用，由此弥补了支付宝社交短板，提升了用户使用支付宝的频次，增强了支付宝对用户的黏性；同时，落实了蚂蚁金服绿色金融战略的重要内容——建立个人碳账户；另外，通过生态扶贫行动，蚂蚁森林还为阿里巴巴电商平台输送了多种绿色生态商品。不过，截至2020年年初，蚂蚁森林平台主要还是依靠蚂蚁金服的投资来运营，这让其产品经理祖望感到挑战颇大：蚂蚁森林是否能实现自主持续增长？应该如何打造自主可持续的公益平台？
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".