Microbiota effects in a Pink1-/- mouse model of infection-induced Parkinson’s disease
Bibliographic record
Abstract
Colon epithelial cell extraction, RNA extraction, whole cDNA synthesis, and qPCR was performed by Morgane Brouillard-Galipeau with training from Jessica Pei. C. rodentium inoculum culture was performed by Morgane Brouillard-Galipeau, mouse infection was performed by Lucia Guerra, and fecal bacterial load measurement was performed by Morgane Brouillard-Galipeau. Lipocalin ELISA was performed by Morgane Brouillard-Galipeau.The Y-maze was conducted by Alexandra Kazanova and Morgane Brouillard-Galipeau.The pole test was conducted by Alexandra Kazanova and Lucia Guerra.The open field test was conducted by Sriparna Mukherjee and Amandine Even from the Trudeau Lab, Université de Montréal.Fecal pellets for 16S sequencing were collected by Morgane Brouillard-Galipeau and by Tyler Cannon (for the 2019 cohort).Fecal DNA extraction was performed by Caroline Monat from the Cousineau Lab, McGill Centre for Microbiome Research.16S sequencing was performed by the McGill Centre for Microbiome Research.Microbiome bioinformatic analysis
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".