Evaluation of sella turcica types with two different classifications in cone-beam computed tomography
Bibliographic record
Abstract
AMA Yalcin ED, Emin Dogan M, Kotanli S. Evaluation of sella turcica types with two different classifications in cone-beam computed tomography. Journal of Stomatology. 2023;76(2):117-121. doi:10.5114/jos.2023.128816. APA Yalcin, E. D., Emin Dogan, M., & Kotanli, S. (2023). Evaluation of sella turcica types with two different classifications in cone-beam computed tomography. Journal of Stomatology, 76(2), 117-121. https://doi.org/10.5114/jos.2023.128816 Chicago Yalcin, Eda D, Mehmet Emin Dogan, and Sedef Kotanli. 2023. "Evaluation of sella turcica types with two different classifications in cone-beam computed tomography". Journal of Stomatology 76 (2): 117-121. doi:10.5114/jos.2023.128816. Harvard Yalcin, E., Emin Dogan, M., and Kotanli, S. (2023). Evaluation of sella turcica types with two different classifications in cone-beam computed tomography. Journal of Stomatology, 76(2), pp.117-121. https://doi.org/10.5114/jos.2023.128816 MLA Yalcin, Eda et al. "Evaluation of sella turcica types with two different classifications in cone-beam computed tomography." Journal of Stomatology, vol. 76, no. 2, 2023, pp. 117-121. doi:10.5114/jos.2023.128816. Vancouver Yalcin E, Emin Dogan M, Kotanli S. Evaluation of sella turcica types with two different classifications in cone-beam computed tomography. Journal of Stomatology. 2023;76(2):117-121. doi:10.5114/jos.2023.128816.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".