On Spatial Structwre of Urban System of Huang-Huai-Hai Plain Based on Radarsat Mosaic Image
Bibliographic record
Abstract
Differ from optical remote sensing, Radar remote sensing has the ability of imaging all time, all weather and penetration to cloud and dry sand and soil Besides penetration, Radar can also detect micro relief, building , vegetation distribution, surface roughness and water content sensitively, thus is superior to other remote sensing means for these studies It has become one of the most important techniques for earth observation Canadian Radarsat was launched in 1996, and is the first commercial operating SAR system It has seven kinds of beam mode and twenty five imaging modes, of which ScanSAR mode can obtain images with swath of 300 km (Narrow) and 500 km (Wide), and resolution of 50m and 100m It conforms to analyses of regional urban geography Based on central place theory, the spatial sturctre of Hrban system of Huang Huai Hai Plain is studied by using Radarsat ScanSAR narrow mode mosaic image spatial structre of The results are: 1) Radarsat ScanSAR data are suitable for automatic extraction of building up area and has meaningful potential for urban geographic study 2) The urban system of Huang huai hai plain, which is deeply influenced by physical factors, especially hydrographic factors( such as river, paleochannel and lake), can be divided into five categories They are: urban system of equal distance between central places on fluvial fan region by Mt Taihangshan; hexagonal urban system in central part of Hebei plain; pentagonal urban system in Huanghe River fluvial fan; quadrilateral urban system in the vicinity of Huaihe River system; and scattered new towns in the places of rolling hills in central and southern in Shandong Province 3) A evolution model of central place system from hexagon to pentagon and to quadrangle governed by river is suggested. 4) No matter hexagonal or pentagonal urban systems, this study has demonstrated that there are good relationship between the model of distance structure of central place and the real life instance
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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 | 0.000 | 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) | 0.001 | 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 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".