MétaCan
Menu
Back to cohort
Record W4383571076 · doi:10.11834/jrs.20187126

Monitoring snow depth based on the SNR signal of GLONASS satellites

2018· article· en· W4383571076 on OpenAlexaboutno aff
Wei Zhou, Lilong Liu, Liangke Huang, Junyu LI, Jun Chen, Fade Chen, Yin XING, Linbo LIU

Bibliographic record

VenueNational Remote Sensing Bulletin · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGLONASSGNSS applicationsGlobal Positioning SystemRemote sensingGeodesyMultipath propagationSatellite systemReflectometrySatelliteReal Time KinematicGeologyComputer sciencePhysicsTelecommunicationsAstronomy

Abstract

fetched live from OpenAlex

利用GNSS-MR(Global Navigation Satellite System Multipath Reflectometry)æŠ€æœ¯åæ¼”ç§¯é›ªæ·±åº¦æ˜¯è¿‘å¹´æ¥ä¸€ç§æ–°å ´çš„å«æ˜Ÿé¥æ„ŸæŠ€æœ¯ã€‚ç›®å‰å¤§å¤šæ•°ç ”ç©¶ä» ä½¿ç”¨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-MRç®—æ³•çš„é›ªæ·±åæ¼”æ¨¡åž‹ã€‚é¦–å ˆï¼Œé€šè¿‡å¤šé¡¹å¼æ‹Ÿåˆåˆ†è§£GLONASS观测数据获取高精度的信噪比残差序列;然后,利用Lomb-Scargleè°±åˆ†æžæ³•å¯¹å ¶è¿›è¡Œé¢‘è°±åˆ†æžå¯è§£ç®—é›ªæ·±å€¼ã€‚é€‰å–IGS中心的YEL2站2015å¹´11月到2016å¹´6æœˆå ±243天的GLONASS卫星L1波段反射信号的SNRæ•°æ®è¿›è¡Œå®žä¾‹åˆ†æžï¼Œå¹¶ä»¥ç¾Žå›½å›½å®¶æ°”è±¡æ•°æ®ä¸­å¿ƒæä¾›çš„åŠ æ‹¿å¤§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)æŠ€æœ¯åæ¼”ç§¯é›ªæ·±åº¦çš„ç²¾åº¦åŒæ ·èƒ½è¾¾åˆ°åŽ˜ç±³çº§ï¼ŒRMSEä» 3.3 cmï¼Œåæ¼”å€¼ä¸Žå®žæµ‹å€¼çš„ç©ºé—´åˆ†å¸ƒè¶‹åŠ¿ä¸€è‡´ä¸”ç›¸å ³æ€§è¾ƒå¼ºï¼Œå ¶ç›¸å ³ç³»æ•°<italic>R</italic><sup>2</sup>高达0.969;(2) ä¸åŒçš„ç§¯é›ªæ·±åº¦å¯¹ä¿¡å™ªæ¯”çš„æŒ¯å¹ é¢‘çŽ‡ä¸Žåž‚ç›´åå°„è·ç¦»å ·æœ‰ç›´æŽ¥å½±å“ï¼›(3) å¯¹åŒä¸€å«æ˜Ÿè€Œè¨€ï¼Œä¿¡å™ªæ¯”çš„é¢‘è°±æŒ¯å¹ å¼ºåº¦å³°å€¼ä¸Žå ¶å¯¹åº”çš„åæ¼”å€¼å­˜åœ¨çº¿æ€§ç›¸å ³ï¼›(4) 在相同条件下,采用多颗GLONASS卫星数据比单颗GLONASSå«æ˜Ÿæ•°æ®åæ¼”é›ªæ·±çš„æ•ˆæžœæ˜Žæ˜¾æ›´ä¼˜ã€‚åŸºäºŽåæ¼”çš„é«˜æ—¶é—´åˆ†è¾¨çŽ‡äº§å“ï¼Œåˆ†æžè¯¥åœ°åŒºé›ªæ·±æ—¥å˜åŒ–çš„æƒ å†µï¼Œå®žéªŒç»“æžœè¡¨æ˜ŽåŸºäºŽé™†åŸºCORS站的GLONASS-MRæŠ€æœ¯åœ¨ç”¨äºŽå®žæ—¶ã€è¿žç»­çš„é›ªæ·±å˜åŒ–ç›‘æµ‹æ–¹é¢å ·æœ‰è‰¯å¥½çš„æ½œåŠ›å’Œå¯è¡Œæ€§ã€‚

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.248
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueNational Remote Sensing BulletinSame topicCryospheric studies and observationsFrench-language works237,207