Experts Speak Forum: Decarbonization for Green Biobanking—The Current Landscape and Challenges for the Future
Bibliographic record
Abstract
This article provides an update on the current state of decarbonization efforts in biobanking, reflecting on the evolving discussions at the annual meetings of the International Society for Biological and Environmental Repositories (ISBER). Following roundtable discussions at the 2021 and 2023 meetings, the 2024 ISBER meeting, which was held in Melbourne, Australia, featured a symposium, workshop, and posters dedicated to decarbonization, highlighting its growing significance on a global scale. The introduction of the term "green biobanking" at this meeting marked a pivotal moment, revealing that current decarbonization efforts in biobanking are largely piecemeal and lack a comprehensive, strategic approach. The goal of this article is to clarify the concept of green biobanking and update the community on the latest developments, while carefully outlining potential pathways for future innovation without pressuring biobankers to adopt existing technologies. The discussion includes insights from experts across various sectors. These include experts from the World Health Organization, biobanks in high-income countries, and low- and middle-income countries, as well as from the Canadian academia, preservation technology, industry, artificial intelligence, and education sectors. The authors emphasize the need for collaboration among all stakeholders to drive the field forward through creative disruption and pave the way for a sustainable future in biobanking.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".