Dante’s kindling box
Bibliographic record
Abstract
Almost half of the Canadian landscape is made up of forests, but the amount of forest surface area burned every year has been growing steadily since 1960.1 This can be problematic due to the effects that forest fires have not only on the local environment but also on the globe as a whole. A forest fire or vegetation fire is defined as any open fire of vegetation such as savannah, forest, agriculture, or peat that is initiated by humans or nature.2 Vegetation fires contribute heavily to air pollution and climate change and are in turn exacerbated by them as well. Air pollution increases due to emissions from these fires, which contain 90-95% carbon dioxide and carbon monoxide as well as methane and other volatile compounds.2 Emissions from forest fires also contribute to global greenhouse gases and aerosol particles (biomass burning organic aerosols),2 leading to indirect and direct consequences to human health. In contrast to biomass burning for household heating and cooking, catastrophic events of forest fires and sweeping grassland fires result in unique exposures and health consequences. In this case report, the relationship between environmental hazardous air pollutants and the potential physiological and psychological health effects associated with the forest fire that affected Fort McMurray, AB in May 2016 are considered.
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.588 | 0.170 |
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".