Bibliographic record
Abstract
Folyóirat-referátumok. Anyagcsere-betegségek \nVérnyomáscsökkentő-kombinációk \nmetabolikus hatásainak \nösszehasonlítása a hazai ACES \nvizsgálatban [The results of ACES \n(Antihypertensive Combinations’ \nLong Term Efficacy Comparing \nStudy): Analysis of metabolic \neffects of antihypertensive \ncombination therapies] \nNádházi Z, Dézsi CA. \n(Levelező szerző: Cs. A. Dézsi, \nDepartment of Cardiology, Petz \nAladár County Teaching Hospital, \nVasvári Pál str. 2–4, Győr, 9024, \nMagyarország; \ne-mail: dcsa62@gmail.com): \nClin Drug Investig. \n2016; 36: \n819–827. | Pulmonológia \nA fizikai edzés hatása a krónikus \nobstruktív tüdőbetegségre [Effects \nof exercise training in patients with \nchronic obstructive pulmonary \ndisease – a narrative review for \nFYSS (Swedish Physical Activity \nExercise Prescription Book)] \nEmtner M, Wadell K. \n(Department of \nNeuroscience, Physiotherapy, Uppsala \nUniversity, Box 593, BMC, Uppsala \n751 24, Svédország; e-mail: \nmargareta.emtner@neuro.uu.se): \nBr J Sports Med. \n 2016; 50: \n368–371. | Hepatológia \nFizikai aktivitás és májbetegségek \n(Physical activity and liver diseases) \nBerzigotti A, Saran U, Dufour JF. \n(Hepatology, University Clinic for \nVisceral Surgery and Medicine, \nInselspital, University of Bern, \nCH-3010 Bern, Svájc; e-mail: \nanalisa.berzigotti@insel.ch): \nHepatology \n 2016, 63: 1026–1040.
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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 | 0.003 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.687 | 0.519 |
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".