Building Research Support Capacity across Human Health Biobanks during the COVID-19 Pandemic
Bibliographic record
Abstract
Human health biobanks are forms of research infrastructure that supply biospecimens and associated data to researchers, and therefore juxtapose the activities of clinical care and biomedical research. The discipline of biobanking has existed for over 20 years and is supported by several international professional societies and dedicated academic journals. However, despite both rising research demand for human biospecimens, and the growth of biobanking as an academic discipline, many individual biobanks continue to experience sustainability challenges. This commentary will summarize how the COVID-19 pandemic is creating new challenges and opportunities for both the health biobanking sector and the supporting discipline of biobanking. While the challenges for biobanks may be numerous and acute, there are opportunities for both individual biobanks and the discipline of biobanking to embrace change such that biobanks can continue to support and drive biomedical research. We will therefore describe numerous practical steps that individual biobanks and/or the discipline of biobanking can take to survive and possibly thrive in response to the COVID-19 pandemic.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".