Bibliographic record
Abstract
成立于2014年的超级猩猩，是一家新兴的健身房品牌。他们打破了传统健身房的年卡模式，坚持“按次付费，不办年卡；专业教练，没有推销”的原则。初期，超级猩猩推出了集装箱健身舱，为顾客提供了独特的健身体验，随后将业务重心转向了团体课程市场。 超级猩猩的健身房，是一个充满活力的团课空间，学员们可以跟随专业教练的动作，在沉浸式的灯光和音乐氛围中，尽情享受一场充满热情的健身派对。这种独特的健身体验将原本孤独乏味的锻炼活动，转变成一场充满活力的社交盛宴。截至2022年底，超级猩猩已经吸引了超过50万名付费用户，在一线和新一线城市开设或筹备了近200家门店。 然而，随着市场竞争的激烈，超级猩猩也面临着全新的挑战——开店速度的问题。与超级猩猩同时期成立的竞争对手乐刻运动，2023年的门店数量已达到1300家。超级猩猩不得不慎重考虑：是维持健身服务质量，确保用户满意度，还是加快开店速度，以争取更大的市场份额？这个两难选择对他们的智慧和决策能力提出了严峻挑战。
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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.335 |
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".