Quiz<br>What is your diagnosis?
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Oruba Z, Kaczmarzyk T, Urbańczyk K, et al. QuizWhat is your diagnosis?. Polish Journal of Pathology. 2018;69(1):107-107. doi:10.5114/pjp.2018.75637. APA Oruba, Z., Kaczmarzyk, T., Urbańczyk, K., Jurczyszyn, A., Fornagiel, S., & Gałązka, K. et al. (2018). QuizWhat is your diagnosis?. Polish Journal of Pathology, 69(1), 107-107. https://doi.org/10.5114/pjp.2018.75637 Chicago Oruba, Zuzanna, Tomasz Kaczmarzyk, Katarzyna Urbańczyk, Artur Jurczyszyn, Szymon Fornagiel, Krystyna Gałązka, and Anna Bednarczyk et al. 2018. "QuizWhat is your diagnosis?". Polish Journal of Pathology 69 (1): 107-107. doi:10.5114/pjp.2018.75637. Harvard Oruba, Z., Kaczmarzyk, T., Urbańczyk, K., Jurczyszyn, A., Fornagiel, S., Gałązka, K., Bednarczyk, A., and Chomyszyn-Gajewska, M. (2018). QuizWhat is your diagnosis?. Polish Journal of Pathology, 69(1), pp.107-107. https://doi.org/10.5114/pjp.2018.75637 MLA Oruba, Zuzanna et al. "QuizWhat is your diagnosis?." Polish Journal of Pathology, vol. 69, no. 1, 2018, pp. 107-107. doi:10.5114/pjp.2018.75637. Vancouver Oruba Z, Kaczmarzyk T, Urbańczyk K, Jurczyszyn A, Fornagiel S, Gałązka K et al. QuizWhat is your diagnosis?. Polish Journal of Pathology. 2018;69(1):107-107. doi:10.5114/pjp.2018.75637.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.261 | 0.157 |
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".