{"id":"W4406333674","doi":"10.3390/proceedings2024110030","title":"Learnable Weight Graph Neural Network for River Ice Classification","year":2025,"lang":"en","type":"article","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"Global Water Futures; Alliance de recherche numérique du Canada; Government of Canada","keywords":"Computer science; Artificial intelligence; Synthetic aperture radar; Graph; Convolutional neural network; Artificial neural network; Pattern recognition (psychology); Remote sensing; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002344783,0.0006925599,0.0004198121,0.0004812417,0.0002040978,0.0004021805,0.0007590337,0.0006012987,0.001071518],"category_scores_gemma":[0.0008523344,0.0002577664,0.0005052268,0.0005831212,0.0003265734,0.0008755586,0.0003557576,0.0008073074,0.0002611004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009370673,"about_ca_system_score_gemma":0.0006426993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01860855,"about_ca_topic_score_gemma":0.01812734,"domain_scores_codex":[0.9998944,0.00001605475,0.000005266151,0.00003903856,0.00002099838,0.00002435076],"domain_scores_gemma":[0.9998191,0.00006643311,0.0000274854,0.00001688411,0.00005764306,0.00001244435],"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":[0.00008569361,0.00007376142,0.001830028,0.00003722916,0.0000485596,0.00006353843,0.00002610092,0.8878832,0.00358573,0.003197687,0.002035229,0.1011331],"study_design_scores_gemma":[0.000001165027,0.000005863813,0.0001222124,0.000001236523,0.000003010533,0.000003663449,0.000001682767,0.998635,0.0003172193,0.0008120338,0.00009522834,0.000001696379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1842803,0.001583223,0.8053268,0.00072637,0.0002236929,0.00007853407,0.0005192085,0.002365738,0.004896215],"genre_scores_gemma":[0.9131016,0.0005255099,0.07999783,0.0002155129,0.00006451904,0.00007461306,0.0009150772,0.00007155924,0.005033698],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01860855,"threshold_uncertainty_score":0.03700048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344469123956871,"score_gpt":0.2194836328649866,"score_spread":0.2060389416254179,"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."}}