{"id":"W4408281123","doi":"10.1109/iecon55916.2024.10905482","title":"Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas","year":2024,"lang":"en","type":"article","venue":"","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Graph; Segmentation; Natural language processing; Artificial intelligence; Net (polyhedron); Theoretical computer science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000859851,0.00008782579,0.0001040073,0.0002445133,0.0007504818,0.0002549558,0.00008314163,0.00006066901,0.00005660761],"category_scores_gemma":[0.00004623891,0.00008322437,0.0001448715,0.0003967154,0.0001026124,0.0007504815,0.00002066158,0.00004710674,0.00009198399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007913176,"about_ca_system_score_gemma":0.0000885269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006060178,"about_ca_topic_score_gemma":0.001750681,"domain_scores_codex":[0.9988306,0.00003561235,0.0003022945,0.0001684305,0.0004266665,0.0002363549],"domain_scores_gemma":[0.999497,0.00009368912,0.00006316628,0.00006622837,0.0002340479,0.00004588527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001344638,0.00003355791,0.004715996,0.0004415629,0.0002671005,9.642015e-7,0.04701259,0.001294589,0.0007689282,0.9317768,0.0121854,0.001489021],"study_design_scores_gemma":[0.0009255953,0.0000338102,0.007681221,0.0003315473,0.0001883372,0.000005410663,0.0747124,0.7803063,0.00007968846,0.1299558,0.00524389,0.000536061],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05941524,0.0004419749,0.9164748,0.001788256,0.001419822,0.001170974,0.00003354066,0.0004844279,0.01877104],"genre_scores_gemma":[0.9899811,0.00005369501,0.002160779,0.00008974128,0.0001328241,0.0002207438,0.00005271299,0.000007692057,0.007300763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9305658,"threshold_uncertainty_score":0.5772176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06471258177797935,"score_gpt":0.348169081035953,"score_spread":0.2834564992579737,"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."}}