Theme 5 Human Cell Biology and Pathology
Bibliographic record
Abstract
Background: The Montreal Neurological Institute's Clinical, Biological, Imaging and Genetic (CBIG) collection and patient registry has recruited almost 4000 participants across Canada with neurological conditions as well as healthy controls since 2016.Using the web based LORIS open source database, the open biobank integrates patient and sample data to help scientists around the world to run cutting-edge research projects within an Open Science framework, to advance the understanding of neurological diseases and discover new therapeutic ways to help fight neurological disorders.Objectives: The main objective is to collect de-identified biological material as well as clinical, imaging and genetic information from patients and controls to enable innovative research projects that will advance the understanding of neurological diseases and human health under the Open Science principles.Results: So far, C-BIG has collaborated with more than 100 academic and industrial partners.Each partner has to give a summary data report on the use of samples within an adjustable period of time if no publication has arisen.The Neuro's C-BIG repository is using an open version of LORIS (Open Portal) for data access, including a Data Query Tool for scientists.Three different levels of access are available: Open, Registered and Controlled.Discussion: The C-BIG Repository hopes to improve and facilitate the material and data collection and sharing under the Open Science principles.The long-term goal is to reinforce the recruitment of participants across the world, by integrating the most information possible of these participants in the multimodal database to have a broader picture of neurological disorders, and accelerating research.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.016 |
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".