Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Parzuchowska K. Dreams. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. 2017;26(2):55-56. doi:10.5114/ppn.2017.69372. APA Parzuchowska, K. (2017). Dreams. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii, 26(2), 55-56. https://doi.org/10.5114/ppn.2017.69372 Chicago Parzuchowska, Katarzyna. 2017. "Dreams". Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii 26 (2): 55-56. doi:10.5114/ppn.2017.69372. Harvard Parzuchowska, K. (2017). Dreams. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii, 26(2), pp.55-56. https://doi.org/10.5114/ppn.2017.69372 MLA Parzuchowska, Katarzyna. "Dreams." Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii, vol. 26, no. 2, 2017, pp. 55-56. doi:10.5114/ppn.2017.69372. Vancouver Parzuchowska K. Dreams. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. 2017;26(2):55-56. doi:10.5114/ppn.2017.69372.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.551 | 0.486 |
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".