The CIBERES Pulmonary Biobank Consortium: an opportunity for cooperative international respiratory research
Bibliographic record
Abstract
To the Editors: Research into the pathogenesis of diseases often requires access to appropriate tissue specimens 1. The lung is not an easily accessible organ. Hence, respiratory research is often hampered by the lack of a large number of adequately preserved lung samples harvested from patients whose phenotype had been carefully and consistently characterised 2. Since 2006, the Spanish government has funded a national network for respiratory research (CIBERES), which is currently formed of 34 research groups working cooperatively on the investigation of basic, clinical and epidemiological aspects of the main respiratory diseases (www.ciberes.org) 3. To facilitate translational respiratory research, CIBERES has taken advantage of its multicentric nature and has designed, organised and established a nonprofit CIBERES Pulmonary Biobank Consortium (CPBC) that follows the recommendations of the International Society for Biological and Environmental Repositories 4 and Organisation for Economic Co-operation and Development 5. The CPBC initiative is fully funded by public, competitive research funds, and is similar to the Lung Tissue Research Consortium …
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.026 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".