Bibliographic record
Abstract
İstanbul Teknik Üniversitesi, Türkiye Istanbul Technical University, Turkey Reutlingen Üniversitesi, Almanya Reutlingen University, Germany Concordia Üniversitesi, Kanada Concordia University, Canada İstanbul Üniversitesi, Türkiye Istanbul Technical University, Turkey Çanakkale Onsekiz Mart Üniversitesi, Türkiye Çanakkale Onsekiz Mart University, Turkey Dokuz Eylül Üniversitesi, Türkiye Dokuz Eylul University, Turkey Tennessee Üniversitesi, ABD Tennessee University, USA Akdeniz Üniversitesi, Türkiye Akdeniz University, Turkey Friedrich-Alexander Üniversitesi, Almanya Friedrich-Alexander University, Germany Selanik Aristotle Üniversitesi, Yunanistan Aristotle University of Thessaloniki, Greece Queensland Teknoloji Üniversitesi, Avustralya Queensland University of Technology, Australia Curtin Üniversitesi, Avustralya Curtin University,Australia Azerbeycan Bilimler Akademisi, Azerbaycan Azerbaijan Academy of Sciences, Azerbaijan Dokuz Eylül Üniversitesi, Türkiye Dokuz Eylul University, Turkey Karadeniz Teknik Üniversitesi, Türkiye Karadeniz Technical University, Turkey Seul Ulusal Üniversitesi, Güney Kore Seoul National University, Korea Orta Doğu Teknik Üniversitesi, Türkiye Middle East Technical University, Turkey Gazi Üniversitesi, Türkiye Gazi University, Turkey Environment Canada, Kanada Environment Canada, Canada
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.175 | 0.108 |
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".