Boreal forest fire monitoring by GNSS, referring to the 2011 BORTAS experiment
Bibliographic record
Abstract
During 2010 and 2011, BORTAS (Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites) experiment has been carried out over northern America and Canada with the aim of studying the air masses which contain emission products from boreal wildfires [1]. In this study, the goal is to understand the potential of ground-based GNSS sensors in monitoring fire plumes. In relatively stable weather condition, strong correlation between GNSS-ZTD (Zenith Total Delay) and PM (Particulate Matter) can be found [2]; reflecting ZHD (Zenith Hydrostatic Delay) the delay caused by the standard dry atmosphere, the delay caused by PM is included in ZWD (Zenith Wet Delay) [3]. Referring to the 2011 BORTAS experiment, data from GNSS ground-based sensors located in the plume trajectories have been analysed. To evaluate the GNSS approach sensitivity, fresh plumes, aged plumes, and background plumes have been studied considering different flights. [1] Palmer, P. I., Parrington, M., Lee, J. D., Lewis, A. C., Rickard, A. R., Bernath, P. F., ... & Young, J. C. (2013). Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) experiment: design, execution and science overview. Atmospheric Chemistry and Physics, 13(13), 6239-6261.[2] Guo, M., Zhang, H., & Xia, P. (2020). A method for predicting short‐time changes in fine particulate matter (PM2. 5) mass concentration based on the global navigation satellite system zenith tropospheric delay. Meteorological Applications, 27(1), e1866.[3] Guo, J., Hou, R., Zhou, M., Jin, X., Li, C., Liu, X., & Gao, H. (2021). Monitoring 2019 forest fires in southeastern australia with GNSS technique. Remote sensing, 13(3), 386.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".