Effects of source temporal resolution on transport simulations of boreal fire emissions
Bibliographic record
Abstract
The quality of temporal information from daily burned area inputs was evaluated using a transport and chemistry experiment. Carbon monoxide emissions from boreal forest fires were estimated using burned area inputs with daily resolution. Averaging of emissions data to create 30‐day aggregate data reduced the variance by 80%, indicating a substantial loss of information. Data from Russia, Canada, and Alaska were tested for periodicity to uncover systematic gaps in daily data. Some evidence of periodicity was found in data from Alaska, where temporal information came from fire mapping by the Alaskan Fire Service. Autocorrelation decayed rapidly and nearly monotonically for Canada and Russia, where temporal information came from Advanced Very High Resolution Radiometer (AVHRR) satellite observations. Daily data as well as 7‐day and 30‐day aggregates were used as input to the University of Maryland Atmospheric Chemistry and Transport Model, and output was compared with CO observations from the Cooperative Air Sampling Network (CASN); continuous measurements from Mace Head, Ireland; and total column CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) instrument. CASN flask measurements showed no sensitivity to high‐frequency variability in the source, indicating the effectiveness of the filtering protocol at ensuring only well‐mixed air masses are sampled in this data set. Differences between daily and 7‐day simulations were too small for quantitative comparison in any of the data. For cases where the differences were substantial, simulations using daily and 7‐day average sources agreed better with observations than 30‐day average sources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".