{"id":"W4280518736","doi":"10.18280/isi.270212","title":"A Novel Architecture Implementation Using Multi Scale Shared Residual Network from Remote Sensing Images for Extracting Water Bodies","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Residual; Computer science; Convolution (computer science); Scale (ratio); Flood myth; Feature (linguistics); Feature extraction; Segmentation; Artificial intelligence; Encoder; Property (philosophy); Real-time computing; Data mining; Pattern recognition (psychology); Remote sensing; Artificial neural network; Algorithm; Geology; Geography; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001897835,0.000455076,0.0002339296,0.0003509337,0.0002195842,0.0004177188,0.0009601343,0.0003492952,0.002957557],"category_scores_gemma":[0.0002740408,0.0001773698,0.0002963674,0.0002721528,0.0001678196,0.0007822597,0.0004554781,0.0003017707,0.0008497013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003621427,"about_ca_system_score_gemma":0.0004304428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005122922,"about_ca_topic_score_gemma":0.008523915,"domain_scores_codex":[0.9998877,0.00001180947,0.00000723783,0.00003358412,0.00003757453,0.00002204473],"domain_scores_gemma":[0.9999037,0.00001249364,0.000009593769,0.00001971839,0.00004610084,0.000008490778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000421131,0.0001836719,0.002354867,0.0002129961,0.0001871642,0.0004038869,0.0001421371,0.1039274,0.2061303,0.007608663,0.008520202,0.6699077],"study_design_scores_gemma":[0.00002391464,0.0002226062,0.001208701,0.00001302778,0.00005335073,0.0002098327,0.00003354785,0.9394761,0.05051369,0.001928063,0.006293175,0.00002402396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05782647,0.0003790129,0.9323362,0.0001673488,0.0001263706,0.00007420715,0.000183344,0.003761776,0.005145243],"genre_scores_gemma":[0.6447847,0.0002671496,0.3446412,0.0001391078,0.00006228939,0.00009811859,0.0007385745,0.0001009628,0.009167923],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005122922,"threshold_uncertainty_score":0.0101862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02249861694171966,"score_gpt":0.269888830725499,"score_spread":0.2473902137837793,"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."}}