ALLERGY, ASTHMA & CLINICAL IMMUNOLOGY MEETING ABSTRACT Open Access Knowledge translation opportunities in allergic
Bibliographic record
Abstract
Network, is a national, multi-disciplinary, multi-sectoral network for research and discovery, knowledge translation and capacity building. AllerGen is dedicated to improving the quality of life for allergy asthma and related immune disease sufferers by supporting research that leads to new diagnostic tests, better medications, more effective public policies and an increase in the number of medical professionals researching and practicing in this area. The Networks of Centres of Excellence (NCE) program, of which AllerGen is a part, is a strategic initiative aligned with Canada’s Science and Technology strategy. The NCE program aims to close the ‘development-to-delivery’ gap, and accelerate the rate at which research results contribute to new, evidence-based, costeffective policies, products and services that generate social and economic benefits for Canadians. The burden of allergy, asthma and related immune disease is significant and growing world-wide, and while the underlying causes of these diseases are actively being studied, the origins of these diseases are still not well understood. According to the results of the International
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.177 | 0.034 |
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".