Bibliographic record
Abstract
사이먼프레이저대학교(SFU: Sim on Fraser University)는 탐험가 사이먼프레이저(Sim on Fraser)의 이름을 따서 1965년에 설립된 대학으로, 지난 40여 년 동안 교양 과목과 과학 분야뿐 아니라 학제적 전문가 프로그램에서 국제적인 강점을 가지고 있다. SFU는 지난 1993년부터 2000년 사이에 Maclean's Magazine의 대학 순위에서 5회 Canada's best com-prehensive university라는 평가를 받을 정도로 캐나다에서는 매우 높은 입지를 굳히고 있는 대학 중 하나이다. SFU는 세 개의 캠퍼스로 나누어져 있는데, 메인 캠퍼스가 Bumaby캠퍼스이고, 나버지가 벤쿠버에 있는 Harbour Center 캠퍼스, 그리고 Surrey 캠퍼스이다. 현재 SFU에는 약 25,000명의 학부생과 대학원생이 재학하고 있다.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".