Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Joob B, Wiwanitkit V. Ultrasound and screening tool for dengue fever. Polish Journal of Radiology. 2018;83:576-576. doi:10.5114/pjr.2018.81253. APA Joob, B., & Wiwanitkit, V. (2018). Ultrasound and screening tool for dengue fever. Polish Journal of Radiology, 83, 576-576. https://doi.org/10.5114/pjr.2018.81253 Chicago Joob, Beuy, and Viroj Wiwanitkit. 2018. "Ultrasound and screening tool for dengue fever". Polish Journal of Radiology 83: 576-576. doi:10.5114/pjr.2018.81253. Harvard Joob, B., and Wiwanitkit, V. (2018). Ultrasound and screening tool for dengue fever. Polish Journal of Radiology, 83, pp.576-576. https://doi.org/10.5114/pjr.2018.81253 MLA Joob, Beuy et al. "Ultrasound and screening tool for dengue fever." Polish Journal of Radiology, vol. 83, 2018, pp. 576-576. doi:10.5114/pjr.2018.81253. Vancouver Joob B, Wiwanitkit V. Ultrasound and screening tool for dengue fever. Polish Journal of Radiology. 2018;83:576-576. doi:10.5114/pjr.2018.81253.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.046 |
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".