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？
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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; both teacher heads agree on what is shown here.
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".