{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005962992,0.001213648,0.0009075194,0.0005374554,0.0004403249,0.001123444,0.001581611,0.001243143,0.005495646],"category_scores_gemma":[0.002361017,0.0004579223,0.0008432082,0.0005286542,0.001022084,0.002974392,0.00201821,0.001142575,0.001021842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001043594,"about_ca_system_score_gemma":0.001497323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006503114,"about_ca_topic_score_gemma":0.00643982,"domain_scores_codex":[0.9994948,0.00005792376,0.00002615661,0.0001807534,0.0001168837,0.0001233682],"domain_scores_gemma":[0.9992767,0.0002518283,0.00005648055,0.000226376,0.0001359885,0.0000525826],"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":[0.0003889614,0.0001324384,0.002640486,0.0004134762,0.0001486018,0.0003265854,0.0002001747,0.2570103,0.05666234,0.07307833,0.01205946,0.596939],"study_design_scores_gemma":[0.00001866003,0.00008765506,0.0006304779,0.0000148671,0.00004124201,0.0001704804,0.00001750034,0.9457874,0.0139671,0.03412922,0.005120131,0.00001521771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05063508,0.001189363,0.9278518,0.000703005,0.0001507943,0.00007035429,0.0002307979,0.003739404,0.01542943],"genre_scores_gemma":[0.8019544,0.0006545962,0.1859117,0.000605133,0.0001939169,0.0001117585,0.000478948,0.0003445307,0.009745143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006503114,"threshold_uncertainty_score":0.01838475,"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."}}