{"id":"W3124830746","doi":"10.3390/rs13030366","title":"Remote Sensing Applications for Landslide Monitoring and Investigation in Western Canada","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; BC Hydro","keywords":"Landslide; Toolbox; Variety (cybernetics); Terrain; Remote sensing; Suite; Risk analysis (engineering); Environmental resource management; Geology; Environmental planning; Computer science; Environmental science; Geography; Cartography; Business; Geotechnical engineering","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":[0.0006017702,0.0003850185,0.0001646771,0.001935042,0.002321192,0.001413528,0.0006528915,0.0003181527,0.00189213],"category_scores_gemma":[0.001125029,0.0001858497,0.0001605213,0.003628837,0.0007107171,0.0003177026,0.0005929507,0.0003753529,0.0003005539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01234413,"about_ca_system_score_gemma":0.02379913,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9824751,"about_ca_topic_score_gemma":0.9946016,"domain_scores_codex":[0.9994855,0.00003403537,0.00002269546,0.00005814765,0.0003014839,0.00009818497],"domain_scores_gemma":[0.9991025,0.00008796855,0.00003671449,0.00002296754,0.0006856642,0.00006431217],"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":[0.0003118555,0.0004146948,0.1807091,0.001007739,0.0001270782,0.005858452,0.01237069,0.01830199,0.06434257,0.01019181,0.03159063,0.6747735],"study_design_scores_gemma":[0.00007792353,0.0001790751,0.6923206,0.0006373057,0.0001515041,0.002168139,0.03092997,0.04896401,0.02619278,0.002166657,0.1959625,0.0002494673],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7616722,0.007858737,0.02557245,0.003645075,0.00009908425,0.0008460945,0.005331889,0.001060085,0.1939144],"genre_scores_gemma":[0.9336861,0.006417803,0.03563777,0.0003107277,0.0000170254,0.00008642978,0.001628484,0.0000752157,0.02214038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0175249,"threshold_uncertainty_score":0.08956343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196795266255352,"score_gpt":0.2277539102901784,"score_spread":0.2157859576276248,"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."}}