Monitoring snow depth based on the SNR signal of GLONASS satellites
Bibliographic record
Abstract
利用GNSS-MR(Global Navigation Satellite System Multipath Reflectometry)技术反演积雪深度是近年来一种新兴的卫星遥感技术。目前大多数研究仅使用GPS(Global Position System)数据限制了该技术的发展，为了扩展GNSS-MR算法的应用，介绍了基于GNSS-MR算法的雪深反演模型。首先，通过多项式拟合分解GLONASS观测数据获取高精度的信噪比残差序列；然后，利用Lomb-Scargle谱分析法对其进行频谱分析可解算雪深值。选取IGS中心的YEL2站2015年11月到2016年6月共243天的GLONASS卫星L1波段反射信号的SNR数据进行实例分析，并以美国国家气象数据中心提供的加拿大Y-H (Yellowknife Henderson)气象站的实测雪深数据为真值，将反演雪深与实测雪深进行对比验证。所得实验结果如下：(1) 与GPS卫星的反演值相比，基于GLONASS-MR (GLONASS Multipath Reflectometry)技术反演积雪深度的精度同样能达到厘米级，RMSE仅3.3 cm，反演值与实测值的空间分布趋势一致且相关性较强，其相关系数R2高达0.969；(2) 不同的积雪深度对信噪比的振幅频率与垂直反射距离具有直接影响；(3) 对同一卫星而言，信噪比的频谱振幅强度峰值与其对应的反演值存在线性相关；(4) 在相同条件下，采用多颗GLONASS卫星数据比单颗GLONASS卫星数据反演雪深的效果明显更优。基于反演的高时间分辨率产品，分析该地区雪深日变化的情况，实验结果表明基于陆基CORS站的GLONASS-MR技术在用于实时、连续的雪深变化监测方面具有良好的潜力和可行性。
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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 | 0.000 |
| 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 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".