{"id":"W2804943494","doi":"10.3390/rs10060844","title":"Staring Spotlight TerraSAR-X SAR Interferometryfor Identification and Monitoring of Small-ScaleLandslide Deformation","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Landslide; Remote sensing; Interferometric synthetic aperture radar; Terrain; Synthetic aperture radar; Geology; Digital elevation model; Interferometry; Radar; Scale (ratio); Geodesy; Geomorphology; Cartography; Geography; Computer science; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002840891,0.0005712117,0.0001689454,0.0007240995,0.0002493725,0.0004130132,0.0003276933,0.0001948406,0.001188989],"category_scores_gemma":[0.0002596229,0.0001624603,0.0001763079,0.0007884394,0.0002608399,0.0002782683,0.0002840112,0.0001919831,0.0003629727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002563296,"about_ca_system_score_gemma":0.0004682406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01068146,"about_ca_topic_score_gemma":0.0449556,"domain_scores_codex":[0.999886,0.00001405945,0.000004303366,0.00001900013,0.00005856539,0.00001810672],"domain_scores_gemma":[0.9998733,0.00001713713,0.00002702453,0.0000249156,0.0000483395,0.000009258463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001674776,0.0001679879,0.0609115,0.0003021021,0.00008693911,0.0002357845,0.0003470358,0.04999398,0.3297715,0.001860522,0.004246842,0.5519083],"study_design_scores_gemma":[0.00004636749,0.0004072966,0.2529941,0.00003856161,0.0001161667,0.0008657385,0.0004617393,0.5402017,0.1860345,0.001517932,0.0172277,0.00008823986],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6395652,0.001378227,0.3448439,0.0003592433,0.00009290958,0.0001986575,0.001029538,0.001932781,0.01059946],"genre_scores_gemma":[0.7145905,0.001178553,0.2784928,0.00009259628,0.00003730735,0.00005719646,0.001240158,0.0001007665,0.004210137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01068146,"threshold_uncertainty_score":0.02123857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130218848117383,"score_gpt":0.2308268628456896,"score_spread":0.2178049780339513,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}