Reviewing global estimates of surface reactive nitrogen concentration and deposition using satellite retrievals
Bibliographic record
Abstract
Since the industrial revolution, human activities have dramatically changed the nitrogen (N) cycle in natural systems. Anthropogenic emissions of reactive nitrogen (N r ) can return to the earth's surface through atmospheric N r deposition. Increased N r deposition may improve ecosystem productivity. However, excessive N r deposition can cause a series of negative effects on ecosystem health, biodiversity, soil, and water. Thus, accurate estimations of N r deposition are necessary for evaluating its environmental impacts. The United States, Canada and Europe have successively launched a number of satellites with sensors that allow retrieval of atmospheric NO 2 and NH 3 column density and therefore estimation of surface N r concentration and deposition at an unprecedented spatiotemporal scale. Atmosphere NH 3 column can be retrieved from atmospheric infra-red emission, while atmospheric NO 2 column can be retrieved from reflected solar radiation. In recent years, scientists attempted to estimate surface N r concentration and deposition using satellite retrieval of atmospheric NO 2 and NH 3 columns. In this study, we give a thorough review of recent advances of estimating surface N r concentration and deposition using the satellite retrievals of NO 2 and NH 3 , present a framework of using satellite data to estimate surface N r concentration and deposition based on recent works, and summarize the existing challenges for estimating surface N r concentration and deposition using the satellite-based methods. We believe that exploiting satellite data to estimate N r deposition has a broad and promising prospect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".