The spatial-temporal distribution of NO_2 in Lanzhou city based on RS
Bibliographic record
Abstract
Taking 1986、1993、2000 and 2006 Landsat/TM images as the data source,and using the negative-correlativity characteristics between the concentration of NO2 and DN,regression-analysis is also employed to make model.Because of the absorb-apex-paddy-structure of NO2 is blue-light-wave,the model-maker of ERDAS is applied to the studies of spatial model and the spatial-temporal distribution of atmospheric NO2 in Lanzhou City of Gansu Province.By making the spatial-temporal distribution of NO2 maps,the retrieved results are compared with monitor datas.The results show that the relationship between the distrution of NO2 and DN is inverse correlation.There is severity pollution of NO2 in the regions of Xigu,Anning and Yantan.Relatively,there is light pollution in Chengguan and Qilihe region.The special terrain and basin climatic features,as well as the unreasonable layout of urban spatial structure are the main reason that cause of NO2 pollution.The conclusions provide some references for optimal placement and environmental pollution control of Lanzhou City.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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; a candidate call from one teacher head, 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".