{"id":"W4200029226","doi":"10.3390/rs13244977","title":"Threshold Definition for Monitoring Gapa Landslide under Large Variations in Reservoir Level Using GNSS","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"China Scholarship Council","keywords":"Landslide; GNSS applications; Warning system; Geology; Water level; Stage (stratigraphy); Deformation monitoring; Displacement (psychology); Early warning system; Geodesy; Deformation (meteorology); Seismology; Global Positioning System; Cartography; Computer science; Geography; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0005261219,0.0005219574,0.0003115836,0.001991812,0.0003129931,0.0006714131,0.0004412298,0.0004010937,0.0004994886],"category_scores_gemma":[0.001254922,0.0001569069,0.0002933647,0.0009142238,0.0002165515,0.0006647386,0.0006508469,0.0003090385,0.0001787428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003223938,"about_ca_system_score_gemma":0.0004964364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005237046,"about_ca_topic_score_gemma":0.004932908,"domain_scores_codex":[0.9996283,0.00003692446,0.00005030954,0.0001046958,0.0001125026,0.00006725948],"domain_scores_gemma":[0.9995824,0.0000610522,0.0000848128,0.00003937724,0.0001785025,0.00005386975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005982264,0.000296633,0.5146501,0.0003841719,0.0001666239,0.0008936644,0.000966889,0.09069917,0.09254114,0.004778557,0.004453604,0.2895712],"study_design_scores_gemma":[0.00005653487,0.0002438163,0.3512211,0.00006224484,0.0001390037,0.0004037501,0.0006360155,0.611843,0.03045195,0.001418453,0.003402406,0.0001217301],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8320279,0.000383402,0.1609756,0.0001070648,0.00008339382,0.0001665869,0.001064213,0.001333486,0.00385828],"genre_scores_gemma":[0.9730206,0.0001058376,0.02558252,0.00002055852,0.00001495252,0.00006636144,0.000790116,0.00002785815,0.0003711393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005237046,"threshold_uncertainty_score":0.01041311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07754123799243914,"score_gpt":0.2920268619487617,"score_spread":0.2144856239563225,"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."}}