Expedited modeling of burn events results (EMBER): A screening-level dataset of 2023 ozone fire impacts in the US
Bibliographic record
Abstract
The Expedited Modeling of Burn Events Results (EMBER) dataset consists of 36-km grid-spacing Community Multiscale Air Quality (CMAQ) photochemical modeling for the summer of 2023. For emissions, these simulations utilized representative monthly and day-of-week anthropogenic emissions from a recent year and preliminary day-specific 2023 fire emissions derived using BlueSky pipeline. The base model run simulated ozone concentrations across the contiguous US during Apr 11-Sep 29, 2023. Two zero-out model runs simulated ozone levels that would have occurred in the US (1) in the absence of fire emissions ("Zero Fires") and (2) in the absence of only Canadian wildfire emissions ("Zero Canadian Fires"). Fire impacts on ozone were then estimated as the difference between ozone simulated in the base EMBER run compared to the ozone simulated in each of the zero out model runs. EMBER is presented as a screening level dataset due to the emissions limitations and the 36-km grid-spacing used in these simulations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".