{"id":"W4408686987","doi":"10.32388/1l3te6","title":"Partial Convolution Meets Visual Attention","year":2025,"lang":"en","type":"preprint","venue":"Qeios","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Convolution (computer science); Computer science; Artificial intelligence; Visual attention; Computer vision; Psychology; Neuroscience; Cognition","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":[0.0001069674,0.0001950981,0.0001959089,0.00009530955,0.0001394814,0.00009903589,0.0008574735,0.0001879355,0.00000962618],"category_scores_gemma":[0.00002234843,0.0002125697,0.0001166422,0.0002694165,0.00004004374,0.0001716904,0.001498345,0.0003771071,0.0001303476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077631,"about_ca_system_score_gemma":0.000113226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001271831,"about_ca_topic_score_gemma":0.000006437638,"domain_scores_codex":[0.9984577,0.00007323203,0.0002860904,0.0006976217,0.0002301674,0.0002551971],"domain_scores_gemma":[0.9987838,0.00006481361,0.0001873698,0.0007963101,0.00009858454,0.00006906677],"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":[0.000028542,0.0004576177,0.0008580997,0.000280522,0.0001479452,0.0000224682,0.0003086979,0.04112858,0.005889696,0.670834,0.02508222,0.2549616],"study_design_scores_gemma":[0.0003890717,0.00004832477,0.00385441,0.0002137859,0.00004819149,0.000007054908,0.000005309003,0.8831322,0.00305082,0.04656598,0.06205408,0.0006308362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004610972,0.0001928249,0.9882451,0.002898948,0.001456621,0.0005736444,0.00001260878,0.000504411,0.001504836],"genre_scores_gemma":[0.9494593,0.000110436,0.0464404,0.000502522,0.0005495212,0.0005239218,0.0001144388,0.00001393701,0.002285492],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9448484,"threshold_uncertainty_score":0.866834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02275257340443791,"score_gpt":0.3172489199929464,"score_spread":0.2944963465885084,"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."}}