The American Association for Bronchology and Interventional Pulmonology 2013–2014 Research Award Recipient
Bibliographic record
Abstract
American Association of Bronchology and Interventional Pulmonology is pleased to announce the winner of the second AABIP Research Award: Dr Alex Chee of University of Calgary, Canada. Dr Chee is a clinical assistant professor of medicine in the Division of Pulmonary, Critical Care, and Sleep medicine at the University of Calgary. His research proposal, “comparing the results of conventional biopsy forceps with cryobiopsy in their ability to sample airway walls in asthmatic patients” was selected as the best proposal among several thought-provoking proposals submitted for the AABIP research award this year. I encourage all young investigators to submit their new research proposals or continuation/advancement of last year’s project in 2014 to 2015 through the AABIP Website for 2 new awards of $10,000 each or continuation of last year’s award for up to $25,000.Figure
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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 | 0.005 | 0.011 |
| 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 | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.242 | 0.147 |
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".