Editorial: Cannabinoids in neuroinflammation, neurodegeneration and pain: Focus on non-neuronal cells
Bibliographic record
Abstract
HV is supported through a Canadian Institutes of Health Research Fellowship and is a Michael Smith Health Research BC Research Trainee. VJ is supported through an Alzheimer’s Association Research Grant (AARG) and a Michael J. Fox Foundation for Parkinson’s Research Target Validation grant. KS is supported through the National Science Centre, Poland grant OPUS no. 2016/23/B/NZ7/01143.MT is supported throughNIA Grant RF1AG057247-05, NINDS Grant RF1NS128800-01, the Parkinson’s Foundation Research Center of Excellence Award PF-RCE-1945, the Weston Family Foundation, the Michael J. Fox Foundation for Parkinson’s Research (MJFF-18212, 18891 and 16778) and Aligning Science Across Parkinson’s (ASAP-020621), and UF-Fixel Institute Norman and Susan Fixel Endowment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 0.018 |
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".