Bibliographic record
Abstract
加拿大Alberta省的阿尔伯达超级网络(Alberta SuperNet)在今年第一期IEEE Spectrum杂志上被评为将来宽带网络和业务发展的模型。为追踪这个网络的发展历史，本期专栏找到了美国华尔街公司报告(WSCR）2002年6月采访主要建设商Axia NetMedia公司首席执行官Arthur R．Price先生的访谈文章和其它一些相关资料。阿尔伯达超级网络的信息给我们提供的重要启示是：宽带网络的发展在促进社会信息化的同时，也要做到有利于减小社会的数字鸿沟。21世纪的到来，使日本东京一个城市的电话总数超过整个非洲，欧洲芬兰一个国家的电脑主机总数超过拉丁美洲，
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".