Biomarker‐Based Approach to α‐Synucleinopathies: Lessons from Neuropathology
Bibliographic record
Abstract
Kovacs, Gabor G; Grinberg, Lea T; Halliday, Glenda; Alafuzoff, Irina; Dugger, Brittany N; Murayama, Shigeo; Forrest, Shelley L; Martinez-Valbuena, Ivan; Tanaka, Hidetomo; Kon, Tomoya; Yoshida, Koji; Jaunmuktane, Zane; Spina, Salvatore; Nelson, Peter T; Gentleman, Steve; Alegre-Abarrategui, Javier; Serrano, Geidy E; Paes, Vitor Ribeiro; Takao, Masaki; Wakabayashi, Koichi; Uchihara, Toshiki; Yoshida, Mari; Saito, Yuko; Kofler, Julia; Rodriguez, Roberta Diehl; Gelpi, Ellen; Attems, Johannes; Crary, John F; Seeley, William W; Duda, John E; Keene, C Dirk; Woulfe, John; Munoz, David; Smith, Colin; Lee, Edward B; Neumann, Manuela; White, Charles L; McKee, Ann C; Thal, Dietmar R; Jellinger, Kurt; Ghetti, Bernardino; Mackenzie, Ian R A; Dickson, Dennis W; Beach, Thomas G
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".