The Temporal-Spatial Distribution of Shule River Alluvial Fan Units in China Based on SAR Data and OSL Dating
Bibliographic record
Abstract
Alluvial fans in arid and semi-arid regions can provide important evidence of geomorphic and climatic changes, which reveal the evolution of the regional tectonic activity and environment. Synthetic aperture radar (SAR) remote sensing technology, which is sensitive to geomorphic features, plays an important role in quickly mapping alluvial fan units of different ages. In this paper, RADARSAT-2 (Canada’s C-band new-generation radar satellite) and ALOS-PALSAR (Japan’s advanced land observing satellite, phased array type L-band SAR sensor) data, acquired over the Shule River Alluvial Fan (SRAF), are used to extract backscattering coefficients, scattering mechanism-related information, and polarimetric characteristic parameters. The correlation between these SAR characteristic parameters and fan units of the SRAF of different ages was studied, and the spatial distribution of fan units, since the Late Pleistocene, was extracted based on the Maximum Likelihood classification method. The results prove that (1) some C-band SAR parameters can describe the geomorphic characteristics of alluvial fan units of different ages in the SRAF; (2) SAR data can be used to map the SRAF’s surface between the Late Pleistocene and the Holocene and to extract the spatial distribution of fan units; and (3) the time-spatial distribution of the SRAF can provide valuable information for tectonic and paleoenvironmental research of the study area.
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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) | 0.000 | 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 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".