{"id":"W4382395082","doi":"10.18280/ts.400336","title":"Advancements in Geological Disaster Monitoring and Early Warning Systems: A Deep Learning and Computer Vision Approach","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Hunan Province; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Deep learning; Computer science; Hyperspectral imaging; Warning system; Artificial intelligence; Data science; Remote sensing; Machine learning; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002896918,0.0001500934,0.0001752519,0.000151725,0.00007385113,0.0001198053,0.00004782092,0.00006100964,0.000002706862],"category_scores_gemma":[0.000009477692,0.000143886,0.00001607182,0.0001731501,0.00003585051,0.0002074522,0.00005552685,0.0001981022,0.000009407643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000552233,"about_ca_system_score_gemma":0.000001763443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000581225,"about_ca_topic_score_gemma":2.901088e-7,"domain_scores_codex":[0.9989703,0.00006929375,0.0002611782,0.0002585693,0.0001786685,0.0002620449],"domain_scores_gemma":[0.999746,0.00007452611,0.00003735828,0.00006459813,0.00001715713,0.00006041193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004959298,0.00005862703,0.3175529,0.0005087458,0.00005922834,0.0000619592,0.005268339,0.4951355,0.04554535,0.00007892182,0.00002535951,0.1356555],"study_design_scores_gemma":[0.0004085015,0.00007018518,0.2592764,0.0001110643,0.000005926689,0.000007737131,0.0003342862,0.7394244,0.00006397034,0.000006728498,0.0001686405,0.000122131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9152094,0.000237301,0.08382495,0.00001069235,0.0001261013,0.0002165455,3.631627e-7,0.0002392452,0.0001354312],"genre_scores_gemma":[0.9965881,0.00007286319,0.003128457,0.000002007655,0.000127321,0.00001575606,0.000009161821,0.00002501998,0.00003131594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2442889,"threshold_uncertainty_score":0.58675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0178093847987782,"score_gpt":0.2406793921499505,"score_spread":0.2228700073511724,"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."}}