Bibliographic record
Abstract
Abstract. High latitude dust (HLD) is receiving growing research interest as its relative impact in the Arctic has been elucidated. Precise knowledge of HLD emission locations is limited in both field studies and satellite observations, leading to a general lack of representation in global models. Using the Frequency of Occurrence (FoO) of above-average Dust Optical Depth (DOD > 0.5) from twenty years (2002–2022) of high-resolution MODIS observations derived for this study (0.1° x 0.1°), we present quantitative evidence that dust sources are widespread across the Canadian Arctic. Additionally, we present qualitative supporting evidence from aerosol type ‘dust’ classifications in VIIRS and CALIPSO satellite data products, as well as some challenges of comparing MODIS AOD to two co-located AERONET sites. The HLD hotspots identified in the “Canadian Arctic Dust Belt” correspond to surfaces with high potential for dust emission in the G-SDS-SBM dataset. There are more areas where hotspots are observed but emission potential is low than the opposite case; additionally, two considerable areas of dust emission are identified at lower latitudes in mainland Canada. When spatially averaged across the broad dust producing region (65° N – 85° N, 125° W – 70° W), annual mean time series of FoO of MODIS DOD > 0.5 suggest an increase in the frequency of dustiness in the latter decade, consistent with our understanding that HLD emissions are increasing in a warming climate. These results further motivate model development to include HLD sources and provide an observational basis for evaluating them.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.300 | 0.204 |
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".