Bibliographic record
Abstract
上海极橙医疗科技股份有限公司（以下简称“极橙”）创立于2015年，在天津开设了第一家齿科门诊，当时主要面向成人，但在激烈的竞争中并未脱颖而出。随后，三位创始人经过深入的市场调研，决定聚焦儿童齿科这一细分市场。凭借“帮孩子快乐看牙”“打造儿童齿科的'迪士尼’”的理念，极橙迅速得到广大家长和孩子的认可。截至2022年8月，极橙已在上海、天津、南京开设了12家门店，有3万多名优质会员，主要来自有1～14岁孩子的高知高薪家庭，其中最核心的客群为3～6岁，客单价每年约2000元。 接下来，极橙希望实现进一步增长，以帮助更多孩子“快乐看牙”。近期，公司计划重点在江浙等地的二三线城市开店，以及随着早期的儿童客户成长为青少年，加强相应的业务。长期来看，塔尔盖认为，成为少数中国家庭才能消费的“迪士尼”是不够的，极橙也许应该成为更大众化的儿童齿科“麦当劳”，品质可靠、标准化，但价格不高，可以服务更多人群，参考美国经验，也可以考虑借助齿科保险来覆盖更多中国儿童。极橙未来到底该如何发展？
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 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.348 |
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".