Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Wciórka J. From the Senior Editor. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. 2019;28(4):231-232. doi:10.5114/ppn.2019.92483. APA Wciórka, J. (2019). From the Senior Editor. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii, 28(4), 231-232. https://doi.org/10.5114/ppn.2019.92483 Chicago Wciórka, Jacek. 2019. "From the Senior Editor". Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii 28 (4): 231-232. doi:10.5114/ppn.2019.92483. Harvard Wciórka, J. (2019). From the Senior Editor. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii, 28(4), pp.231-232. https://doi.org/10.5114/ppn.2019.92483 MLA Wciórka, Jacek. "From the Senior Editor." Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii, vol. 28, no. 4, 2019, pp. 231-232. doi:10.5114/ppn.2019.92483. Vancouver Wciórka J. From the Senior Editor. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. 2019;28(4):231-232. doi:10.5114/ppn.2019.92483.
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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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.313 | 0.272 |
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".