Functional resonance as a tool to support diagnosis of schizophrenic patients
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Sylwia Karpiel I, Kurasz Z. Functional resonance as a tool to support diagnosis of schizophrenic patients. Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology. 2021;16(1):51-60. doi:10.5114/nan.2021.108033. APA Sylwia Karpiel, I., & Kurasz, Z. (2021). Functional resonance as a tool to support diagnosis of schizophrenic patients. Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology, 16(1), 51-60. https://doi.org/10.5114/nan.2021.108033 Chicago Sylwia Karpiel, Ilona, and Zofia Kurasz. 2021. "Functional resonance as a tool to support diagnosis of schizophrenic patients". Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology 16 (1): 51-60. doi:10.5114/nan.2021.108033. Harvard Sylwia Karpiel, I., and Kurasz, Z. (2021). Functional resonance as a tool to support diagnosis of schizophrenic patients. Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology, 16(1), pp.51-60. https://doi.org/10.5114/nan.2021.108033 MLA Sylwia Karpiel, Ilona et al. "Functional resonance as a tool to support diagnosis of schizophrenic patients." Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology, vol. 16, no. 1, 2021, pp. 51-60. doi:10.5114/nan.2021.108033. Vancouver Sylwia Karpiel I, Kurasz Z. Functional resonance as a tool to support diagnosis of schizophrenic patients. Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology. 2021;16(1):51-60. doi:10.5114/nan.2021.108033.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".