Bibliographic record
Abstract
过去的房产销售行业模式存在许多弊端：一手新楼盘销售模式客源不足，网络投放广告引源效果差；采用“一二手房联动分销模式”，二手经纪公司过多难以管理；二手房市场同样存在信息化程度低，获取客源与房源成本高、效率低，网上二手房信息真实度低等问题。 行业模式的变革势在必行，房多多创世人段毅这样认为。在他构想中，房多多将是一个“能改变传统房地产销售价值链”的房地产营销平台。到2013年，房多多业务已经覆盖到全国10多个城市，提供专业的一二手联动业务线下与系统的同步服务，并进一步聚焦基于云端（SAAS）模式的互联网中介公司管理运营服务。但是，房多多要在激烈的同行平台竞争中脱颖而出，还面临着众多挑战：如何让中介公司快速感知房多多模式的价值所在？如何让中介公司放心分享房源和房客？如何提高云端模式的兼容性来实现快速扩张？
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.205 |
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".