Neurocutaneous melanosis in a newborn identified prenatally by non-invasive imaging
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Huras H, Hurkała J, Radoń-Pokracka M, et al. Neurocutaneous melanosis in a newborn identified prenatally by non-invasive imaging. Dermatology Review/Przegląd Dermatologiczny. 2018;105(4):558-561. doi:10.5114/dr.2018.78078. APA Huras, H., Hurkała, J., Radoń-Pokracka, M., Dyduch, G., Taczanowska-Niemczuk, A., & Jach, R. et al. (2018). Neurocutaneous melanosis in a newborn identified prenatally by non-invasive imaging. Dermatology Review/Przegląd Dermatologiczny, 105(4), 558-561. https://doi.org/10.5114/dr.2018.78078 Chicago Huras, Hubert, Joanna Hurkała, Małgorzata Radoń-Pokracka, Grzegorz Dyduch, Anna Taczanowska-Niemczuk, Robert Jach, and Jakub Droś et al. 2018. "Neurocutaneous melanosis in a newborn identified prenatally by non-invasive imaging". Dermatology Review/Przegląd Dermatologiczny 105 (4): 558-561. doi:10.5114/dr.2018.78078. Harvard Huras, H., Hurkała, J., Radoń-Pokracka, M., Dyduch, G., Taczanowska-Niemczuk, A., Jach, R., Droś, J., and Lauterbach, R. (2018). Neurocutaneous melanosis in a newborn identified prenatally by non-invasive imaging. Dermatology Review/Przegląd Dermatologiczny, 105(4), pp.558-561. https://doi.org/10.5114/dr.2018.78078 MLA Huras, Hubert et al. "Neurocutaneous melanosis in a newborn identified prenatally by non-invasive imaging." Dermatology Review/Przegląd Dermatologiczny, vol. 105, no. 4, 2018, pp. 558-561. doi:10.5114/dr.2018.78078. Vancouver Huras H, Hurkała J, Radoń-Pokracka M, Dyduch G, Taczanowska-Niemczuk A, Jach R et al. Neurocutaneous melanosis in a newborn identified prenatally by non-invasive imaging. Dermatology Review/Przegląd Dermatologiczny. 2018;105(4):558-561. doi:10.5114/dr.2018.78078.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".