Presentation_1_National Neuroinformatics Framework for Canadian Consortium on Neurodegeneration in Aging (CCNA).PPTX
Bibliographic record
Abstract
<p>The Canadian Institutes for Health Research (CIHR) launched the “International Collaborative Research Strategy for Alzheimer's Disease” as a signature initiative, focusing on Alzheimer's Disease (AD) and related neurodegenerative disorders (NDDs). The Canadian Consortium for Neurodegeneration and Aging (CCNA) was subsequently established to coordinate and strengthen Canadian research on AD and NDDs. To facilitate this research, CCNA uses LORIS, a modular data management system that integrates acquisition, storage, curation, and dissemination across multiple modalities. Through an unprecedented national collaboration studying various groups of dementia-related diagnoses, CCNA aims to investigate and develop proactive treatment strategies to improve disease prognosis and quality of life of those affected. However, this constitutes a unique technical undertaking, as heterogeneous data collected from sites across Canada must be uniformly organized, stored, and processed in a consistent manner. Currently clinical, neuropsychological, imaging, genomic, and biospecimen data for 509 CCNA subjects have been uploaded to LORIS. In addition, data validation is handled through a number of quality control (QC) measures such as double data entry (DDE), conflict flagging and resolution, imaging protocol checks<sup>1</sup>, and visual imaging quality validation. Site coordinators are also notified of incidental findings found in MRI reads or biosample analyses. Data is then disseminated to CCNA researchers via a web-based Data-Querying Tool (DQT). This paper will detail the wide array of capabilities handled by LORIS for CCNA, aiming to provide the necessary neuroinformatic infrastructure for this nation-wide investigation of healthy and diseased aging.</p>
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.060 | 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".