{"id":"W2031601132","doi":"10.1109/tnn.2003.816345","title":"Kerneltron: support vector \"machine\" in silicon","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Support vector machine; Computer hardware; Massively parallel; Very-large-scale integration; Parallel computing; Artificial intelligence; Embedded system","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":[0.0004164776,0.0004838218,0.0003324565,0.0004436538,0.00017795,0.0008503182,0.001174668,0.0006553399,0.009731857],"category_scores_gemma":[0.001454014,0.0002896479,0.0003160049,0.0005450345,0.0003104992,0.001253574,0.0005729437,0.0006669359,0.004516288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003987763,"about_ca_system_score_gemma":0.0005879477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009961374,"about_ca_topic_score_gemma":0.001115896,"domain_scores_codex":[0.9996241,0.00006667394,0.00002495033,0.00005567853,0.0001943485,0.00003428228],"domain_scores_gemma":[0.9996173,0.0001275501,0.00002983359,0.00006213456,0.0001458332,0.00001733926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006822403,0.0001415075,0.001742081,0.0005315796,0.00009823759,0.0003719778,0.0001882609,0.08090707,0.05298854,0.0787701,0.06367406,0.7199043],"study_design_scores_gemma":[0.0001160805,0.000272741,0.000734486,0.00004211695,0.00002601323,0.0003614534,0.00004123991,0.8331804,0.07212675,0.02034344,0.0727149,0.00004029776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01003927,0.0004977608,0.959447,0.0002805186,0.0001413623,0.00007755327,0.0004177097,0.02088322,0.008215683],"genre_scores_gemma":[0.2529164,0.0006825757,0.7201717,0.0003500827,0.0001086014,0.0003562297,0.002237537,0.001051938,0.02212489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009731857,"threshold_uncertainty_score":0.0325563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008405461851561097,"score_gpt":0.2069200729740154,"score_spread":0.1985146111224543,"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."}}