{"id":"W2377644092","doi":"","title":"LANDSLIDE MONITORING BY INSAR","year":2004,"lang":"en","type":"article","venue":"Chinese Journal of Engineering Geophysics","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Interferometric synthetic aperture radar; Landslide; Remote sensing; Decorrelation; Synthetic aperture radar; Geology; Geodesy; Computer science; Seismology; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000307359,0.0006172389,0.0005632133,0.001842274,0.0003498059,0.0006918408,0.0003960646,0.0004961563,0.0008597976],"category_scores_gemma":[0.0003025866,0.0002745104,0.0002633834,0.001785215,0.0001912124,0.0008445965,0.0004431638,0.0004287742,0.0009820019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002379073,"about_ca_system_score_gemma":0.0003157667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002149834,"about_ca_topic_score_gemma":0.003548779,"domain_scores_codex":[0.9996182,0.0000563595,0.00002271393,0.0001162027,0.000145166,0.00004129713],"domain_scores_gemma":[0.9997808,0.00001796183,0.00004360356,0.00004086518,0.0001050138,0.000011745],"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":[0.0002840848,0.0001559442,0.05365264,0.0004810594,0.000279292,0.0003323967,0.0003213544,0.03430336,0.2029576,0.002888031,0.009656036,0.6946883],"study_design_scores_gemma":[0.00009915532,0.0004813476,0.1846276,0.0001384686,0.0005640669,0.0009984027,0.0007242749,0.50675,0.2270218,0.004734423,0.07365154,0.0002088739],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3943977,0.003927672,0.5623097,0.0004234555,0.0002186687,0.0001747374,0.003412643,0.007853508,0.02728185],"genre_scores_gemma":[0.7456953,0.001958902,0.2430558,0.0001416877,0.00009874306,0.00006786356,0.00361028,0.0001761137,0.005195322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002149834,"threshold_uncertainty_score":0.004274607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0037577145268982,"score_gpt":0.1798220355797563,"score_spread":0.1760643210528581,"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."}}