International Alzheimer's Disease Research Portfolio (IADRP) aims to capture global Alzheimer's disease research funding
Bibliographic record
Abstract
Alzheimer's disease (AD) is a recognized international public health crisis. There is an urgent need for public and private funding agencies around the world to coordinate funding strategies and leverage existing resources to enhance and expand support of AD research. To capture and compare their existing investments in AD research and research-related resources, major funding organizations are starting to utilize the Common Alzheimer's Disease Research Ontology (CADRO) to categorize their funding information. This information is captured in the International Alzheimer's Disease Research Portfolio (IADRP) for further analysis. As of January, 2014, over fifteen organizations from the US, Canada, Europe and Australia have contributed their information. The goal of the IADRP project is to enable funding organizations to assess the changing landscape of AD research and coordinate strategies, leverage resources, and avoid duplication of effort.
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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 | 0.032 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.025 | 0.038 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".