{"id":"W2907758873","doi":"10.1080/07038992.2018.1481736","title":"Land Subsidence Monitoring in Greater Vancouver Through Synergy of InSAR and Polarimetric Analysis","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Western University","funders":"Canadian Space Agency","keywords":"Interferometric synthetic aperture radar; Polarimetry; Geography; Subsidence; Remote sensing; Geodesy; Geology; Cartography; Physical geography; Synthetic aperture radar; Geomorphology; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000148647,0.000242213,0.0001968095,0.0009934424,0.000824389,0.0007415091,0.0002892618,0.0001572516,0.0005760698],"category_scores_gemma":[0.0003338662,0.0001701178,0.00008639364,0.001841402,0.0002217563,0.0001614762,0.0004475581,0.000210743,0.0001580222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003495098,"about_ca_system_score_gemma":0.003636901,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9407968,"about_ca_topic_score_gemma":0.988649,"domain_scores_codex":[0.9998277,0.00001470959,0.000006357797,0.00002670455,0.00008592846,0.00003858695],"domain_scores_gemma":[0.9997621,0.00001210459,0.00001724523,0.00000850108,0.0001648525,0.00003516261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003011565,0.000142848,0.845672,0.0001399532,0.0001044647,0.0008422291,0.001288083,0.005999459,0.02633918,0.000242203,0.002031565,0.1168969],"study_design_scores_gemma":[0.00001360213,0.0000302302,0.9856874,0.00001884974,0.00002415993,0.00008161953,0.001229207,0.008616311,0.001398335,0.0000252934,0.002862714,0.00001225279],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995748,0.0001633792,0.0003851563,0.00004297542,0.000003200588,0.00003135723,0.0005888554,0.0000301804,0.003006964],"genre_scores_gemma":[0.996119,0.0002070783,0.00120808,0.00001903069,0.000001899561,0.000009067338,0.0007182055,0.000005830267,0.001711765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05920321,"threshold_uncertainty_score":0.1191037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0112373851600067,"score_gpt":0.220269618178794,"score_spread":0.2090322330187873,"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."}}