Editorial: Air pollution remote sensing and the subsequent interactions with ecology on regional scales
Bibliographic record
Abstract
Air pollution remote sensing and the subsequent interactions with ecology on regional scales With the rapid increase of global population and urbanization, the impact of human activities on the earth's ecological environment has become increasingly serious. Human beings are currently facing unprecedented atmospheric environmental challenges, such as the polar ozone (O 3 ) hole, global warming, haze and photochemical pollution, etc. Air pollution, defined as the release of pollutants into the atmosphere, has been regarded as one of the greatest environmental problems that closely related to our lives due to its significant impacts on the environment and human health. Fortunately, in recent decades, we have deeply realized the harm of air pollution, and the related detection technology and treatment methods have also been greatly improved. In particular, the development of remote sensing, such as radar and satellite, has greatly enhanced our understanding of the spatiotemporal, transmission mechanism, and formation mechanism of air pollution.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".