Miscellaneous. Folyóirat-referátumok. Beszámolók. Könyvismertetés
Bibliographic record
Abstract
Folyóirat-referátumok. Diabetológia \nÉrvizsgálatok diabetesben \n(Vascular imaging in diabetes) \nLevitt, K., Vivas, L., Courtney, B., \net al. \n(Levelező szerző: K. A. \nConnelly, Keenan Research Centre for \nBiomedical Science, St Michael’s \nHospital, University of Toronto, \n209 Victoria Street, Toronto, \nOntario, Kanada M5B 1C6): \nCurr. \nAtheroscler. Rep., \n2014, \n16 \n(4), 399. | Kardiológia \nNagy adagú allopurinol hatása \naz ischaemiás szívbetegségben \nszenvedő betegek balkamra- \ntömegére (High-dose allopurinol \nreduces left ventricular mass \nin patients with ischemic heart \ndisease) \nRekhraj, S., Gandy, S. J., \nSzwejkowski, B. R., et al. \n(Division of Cardiovascular and \nDiabetes Medicine, Medical Research \nInsitute, University of Dundee, \nNinewells Hospital and Medical \nSchool, Dundee, Scotland, \nEgyesült Királyság): \nJ. Am. Coll. \nCardiol., \n2013, \n61 \n(9), 926–932. | Sportorvostan \nEnergiaforrás az emelkedő terhelés \nsorán (Energy system contributions \nduring incremental exercise test) \nBertuzzi, R., Nascimento, E. M., Urso, \nR. P., et al. \n (School of Physical \nEducation and Sport, University \nof São Paulo, Brazília; e-mail: \nbertuzzi@usp.br): \nJ. Sports Sci. \nMed., \n 2013, \n12 \n(3), 454–460. | Beszámolók. „Az ultrahang életet \nmenthet” \nA VIMOR kampánya | A Magyar Williams \nSzindróma Társaság \nfennállásának 15. \névfordulója | Könyvismertetés. Sherwin B. Nuland \nFertőző gyógyítók \nA gyermekágyi láz \nés Semmelweis különös története \nFordította: Garai Attila \nISBN 978-963-304-145-1 \nHVG Kiadó Zrt., Budapest, 2013
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.712 | 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".