Bibliographic record
Abstract
每日瑜伽的创业历程是一个典型的移动互联网创业故事。每日瑜伽成立于2012年，是一家在线瑜伽服务平台，运营“每日瑜伽”App，主要提供线上瑜伽教学课程服务。与传统的创业模式不同，每日瑜伽选择了先在北美市场建立起自己的业务，然后再回归中国市场，并逐渐形成了线上课程、垂直社区、瑜伽电商和线下教练培训四大业务板块。在十余年的发展中，每日瑜伽的用户数量突破了6000万，覆盖了全球212个国家以及中国400多个城市。 然而，作为深耕瑜伽垂直赛道的企业，每日瑜伽既要接受运动“反人性”特质的挑战，又要面对综合类健身平台的竞争，还可能遭遇社交娱乐平台、线下瑜伽馆以及硬件设备厂商等多种商业企业从不同维度发起的“进攻”。用户留存难、商业变现难……每日瑜伽应该如何破局，才能找到新的收入增长点和商业发展机会？
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.020 |
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".