Extensible Database of Validated Biomass Smoke Events for Health Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The extensible Biomass Smoke Validated Events Database is an ongoing, community driven, collection of air pollution events which are known to be caused by vegetation fires such as bushfires (also known as wildfire and wildland fires), or prescribed fuel reduction burns, and wood heaters. This is useful for researchers of health impacts who need to distinguish smoke from vegetation versus other sources. The overarching aim is to study statistical associations between biomass smoke pollution and health. Extreme pollution events may also be caused by dust storms or fossil fuel smog events and so validation is necessary to ensure the events being studied are from biomass. This database can be extended by contribution from other researchers outside the original team. There are several available protocols for adding validated smoke events to the database, to ensure standardization across datasets. Air pollution data can be included, and free software was created for identification of extreme values. Protocols are described for reference material needed as supporting evidence for event days. The utility of this database has previously been demonstrated in analyses of hospitalization and mortality. The database was created using open source software that works across operating systems. The prospect for future extensions to the database is enhanced by the description in this paper, and the availability of these data on the open access Github repository enables easy addition to the database with new data by the research community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it