Editorial: Air pollution remote sensing and the subsequent interactions with ecology on regional scales
Bibliographic record
Abstract
Editorial on the Research Topic Air pollution remote sensing and the subsequent interactions with ecology on regional scalesWith 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.Air pollution events near the ground, such as sandstorms, acid rain, haze and O 3 pollution, etc., can cause great harm to buildings, vegetation, and human health.Generally, the occurrence of these air pollutions has an extensive spatial range and a very random timing.Previous site-based studies can only represent very local information, and there are few long-term continuous observations.As an important approach of monitoring the large-scale atmospheric condition, remote sensing plays a significant role in characterizing the temporal and spatial distributions of air pollution, as well as its multiple feed-back effects on the ecosystem.The continually improved spatial
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.020 |
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".