Neglected malignant neoplasms with cutaneousinvolvement
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Kaminska-Winciorek G, Cybulska-Stopa B, Gajda M, Pawełczak-Szastak M, Gęga-Czarnota A. Neglected malignant neoplasms with cutaneous involvement. Postępy Dermatologii i Alergologii/Advances in Dermatology and Allergology. 2021. doi:10.5114/ada.2021.108457. APA Kaminska-Winciorek, G., Cybulska-Stopa, B., Gajda, M., Pawełczak-Szastak, M., & Gęga-Czarnota, A. (2021). Neglected malignant neoplasms with cutaneous involvement. Postępy Dermatologii i Alergologii/Advances in Dermatology and Allergology. https://doi.org/10.5114/ada.2021.108457 Chicago Kaminska-Winciorek, Grażyna, Bożena Cybulska-Stopa, Maksymilian Gajda, Marta Pawełczak-Szastak, and Adrianna Gęga-Czarnota. 2021. "Neglected malignant neoplasms with cutaneous involvement". Postępy Dermatologii i Alergologii/Advances in Dermatology and Allergology. doi:10.5114/ada.2021.108457. Harvard Kaminska-Winciorek, G., Cybulska-Stopa, B., Gajda, M., Pawełczak-Szastak, M., and Gęga-Czarnota, A. (2021). Neglected malignant neoplasms with cutaneous involvement. Postępy Dermatologii i Alergologii/Advances in Dermatology and Allergology. https://doi.org/10.5114/ada.2021.108457 MLA Kaminska-Winciorek, Grażyna et al. "Neglected malignant neoplasms with cutaneous involvement." Postępy Dermatologii i Alergologii/Advances in Dermatology and Allergology, 2021. doi:10.5114/ada.2021.108457. Vancouver Kaminska-Winciorek G, Cybulska-Stopa B, Gajda M, Pawełczak-Szastak M, Gęga-Czarnota A. Neglected malignant neoplasms with cutaneous involvement. Postępy Dermatologii i Alergologii/Advances in Dermatology and Allergology. 2021. doi:10.5114/ada.2021.108457.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.024 |
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".