{"id":"W2946157752","doi":"10.1007/978-3-030-20351-1_7","title":"Adaptive Graph Convolution Pooling for Brain Surface Analysis","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pooling; Computer science; Graph; Theoretical computer science; Spectral graph theory; Artificial intelligence; Algorithm; Voltage graph; Line graph","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":[0.0003870942,0.001119005,0.0007541609,0.000702565,0.0002906759,0.0008941001,0.001535887,0.0009003155,0.01205024],"category_scores_gemma":[0.0007307676,0.0004418426,0.00111585,0.001476119,0.0003930076,0.001154454,0.001162073,0.001021574,0.005965458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005536861,"about_ca_system_score_gemma":0.0005099431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002889246,"about_ca_topic_score_gemma":0.00607076,"domain_scores_codex":[0.9998105,0.00002979733,0.0000100213,0.00005551001,0.00006594152,0.00002825139],"domain_scores_gemma":[0.9997925,0.00006654999,0.00001163373,0.00006960956,0.00004712517,0.00001253824],"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":[0.00008944528,0.00005016072,0.0001467421,0.0002299634,0.0001255367,0.00009816872,0.00005270145,0.02846478,0.06260435,0.03399812,0.02829538,0.8458446],"study_design_scores_gemma":[0.00001162658,0.00006160671,0.0008737227,0.00002890548,0.0000704647,0.0003517357,0.00002825353,0.8396535,0.05277102,0.06621533,0.03989055,0.00004325323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001737277,0.0005427103,0.9933478,0.0001164271,0.00007948175,0.00001937341,0.000182142,0.001833,0.002141825],"genre_scores_gemma":[0.0552688,0.001597589,0.9183406,0.0001736996,0.0001724179,0.0001149101,0.001224554,0.001488348,0.02161899],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01205024,"threshold_uncertainty_score":0.04031205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03719479559170657,"score_gpt":0.2635181630130012,"score_spread":0.2263233674212946,"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."}}