[Temporal and Spatial Variations in Black Carbon Aerosol in Different Atmospheric Background Stations in China from 2006 to 2020].
Bibliographic record
Abstract
and 0.20±0.13-0.22±0.19, respectively. The interannual variability in BC included differences between different atmospheric background stations, which could be divided into four categories:low interannual variability, such as at the Akedala station; an initial increase followed by a decrease and subsequent stabilization, such as at the Mt. Waliguan station; an initial decrease followed by stabilization, such as at the Shangri-La station; and an initial stabilization followed by a decrease, such as at the Mt. Longfeng, Shangdianzi, Jinsha, and Lin'an stations. Seasonal variations in BC included differences in different atmospheric background stations. The BC mass concentrations were lowest in autumn and higher in winter and spring west of the "Hu Huanyong" line and were highest in winter and lowest in summer east of the "Hu Huanyong" line. BC contributed to the AOD being larger in all stations in the spring and summer and contributed less at the stations west of the "Hu Huanyong" line in autumn and the stations east of the "Hu Huanyong" line in winter. The diurnal variations in BC were mainly bimodally distributed in the different atmospheric background stations, but the peak times varied in different stations and seasons.
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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".