Promoting global collaboration to improve bioaerosol exposure assessment and understanding of associated health impacts: outcomes from a series of workshops
Bibliographic record
Abstract
We are surrounded, in both indoor and outdoor environments, by air containing particles of biological origin (bioaerosols).We constantly inhale them, and, depending upon their size, they deposit in different parts of our airways.Despite their ubiquitous nature and our constant exposure, bioaerosol diversity and composition of the environment are not well characterized, and we understand little about which bioaerosols we are exposed to and how this impacts our health, either positively or negatively.Indoor/Outdoor Bioaerosols Interface and Relationships Network (BioAirNet), a Clean Air Programme-funded network, has recognized the need for the bioaerosol community to reflect on the current challenges facing bioaerosol exposure assessment and the determination of the associated cellular/molecular responses driving specific health outcomes.A series of online workshops for the bioaerosol community were hosted by BioAirNet in September 2022, which aimed to bring together global expertise to discuss the current challenges impeding improved assessment of bioaerosol exposure and understanding of the downstream cellular and molecular mechanisms driving health outcomes by discussing these challenges; considering where we need to be, where we are now and how we get there.Professional facilitation was key to their success, enabling the multidisciplinary bioaerosol community to explore and address these challenges within a focused and productive environment to prioritize themes and agree on action plans for continued momentum following the workshops.These themes were as follows:(1) conceptual model; (2) stakeholder mapping; (3) knowledge transfer; (4) writing project and (5) conference-type event, collectively covering research, knowledge mobilization and networking activities.A subsequent in-person follow-up workshop was held in November 2023.It provided an opportunity to share progress on the five themes, critique what had already been done and act as a launch-pad to progress the actions further.Delegates also had the opportunity to share ongoing or upcoming work, particularly projects requiring input from others, to encourage collaborative working and sharing expertise.The use of facilitated workshops is a valuable tool for all scientific communities to collectively explore and successfully address key issues within their field.
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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 | 0.039 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".