Optical properties of boreal forest fire smoke derived from Sun photometry
Bibliographic record
Abstract
Aerosol optical properties derived from S un photometry were investigated in terms of climatological trends at two S un photometer sites significantly affected by western Canadian boreal forest fire smoke and in terms of a 2‐week series of smoke events observed at stations near and distant from boreal forest fires. Aerosol optical depth (τ a ) statistics for Waskesiu, Saskatchewan, and Thompson, Manitoba, were analyzed for summer data acquired between 1994 and 1999. A significant correlation between the geometric mean and the forest fire frequency indices (hot spots) was found; on the average, 80% of summertime optical depth variation in western Canada can be linked to forest fire sources. The average geometric mean and geometric standard deviation at 500 nm was observed to be 0.074 and 1.7 for the clearest, relatively smoke‐free summer and 0.23 and 3.0 for the summer most influenced by smoke. A systematic decrease of fine mode Angstrom exponent (α f ) was noted ( d α f / d log τ a ∼ −0.6). This decrease roughly corresponds to an increase in the fine mode effective radius ( r eff ) from 0.09 to 0.15 μm and an abundance ( A ) to size rate increase near 2.0 ( d log A / d log r eff ). A 1998 series of forest fire events was tracked using TOMS, AVHRR, and GOES imagery, back trajectories, and data from six Sun photometer sites in Canada and eastern United States. The results showed rates of decrease of α f with increasing τ a which were similar to the climatological data. An analysis in terms of source to station distance showed a decrease in α f and an increase in r eff with increasing distance. This observation was coherent with previous observations on the particle growth effects of aging.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".