Bibliographic record
Abstract
“Hello!Canadian Dunk!”(嘿,加拿大灌篮！)加拿大体育网电视台的解说员老杰克在贾玛尔·马格洛伊尔每完成一次暴扣之后都用独特的嘶哑嗓音忘情高喊。马格洛伊尔是多伦多人,和湖人队三名队长之——的福克斯是老乡,强悍的防守和野兽般的扣篮是他的标签。每次黄蜂队到猛龙队的客场比赛,他得到的鼓掌欢呼绝不亚于“加拿大飞人”维斯·卡特。这个不久以前还默默无闻的小伙子现在和前辈纳什一起成为加拿大体育的旗手。今年他以候补身份入选全明星阵容,虽然有东部中锋实力较为薄弱的因素,但也足以证明此人在全联盟各队教练心目中的分量。没错,他也许不是媒体和球迷宠爱的“偶像派”巨星,不像奥尼尔那样无坚不摧,不像姚明那样万众瞩目,不像艾弗森那样领导潮流,不像科比那样迷倒众生,但绝对是个真才实料如假包换的实力派球星。
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".