{"id":"W2103521987","doi":"","title":"Multiple Cause Vector Quantization","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Vector quantization; Learning vector quantization; Disjoint sets; Computer science; Key (lock); Artificial intelligence; Inference; Quantization (signal processing); Factor (programming language); Set (abstract data type); Algorithm; Pattern recognition (psychology); Data modeling; Mathematics; Discrete mathematics","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":[0.00170878,0.000761512,0.001740193,0.001167056,0.0005644076,0.001865973,0.003081144,0.001738364,0.005312261],"category_scores_gemma":[0.006353803,0.0005173802,0.001451132,0.00206231,0.001423505,0.003662652,0.001685961,0.002766401,0.001144283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001343063,"about_ca_system_score_gemma":0.001457438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005484261,"about_ca_topic_score_gemma":0.005720588,"domain_scores_codex":[0.9983312,0.0004573581,0.00007762578,0.0004665958,0.000511328,0.0001558239],"domain_scores_gemma":[0.9984162,0.0006451273,0.0001692582,0.0003623001,0.000328748,0.00007837853],"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":[0.0001493647,0.00010044,0.002954111,0.0003016887,0.0001763072,0.0001445617,0.0002757989,0.3718711,0.001947149,0.3746116,0.01444983,0.233018],"study_design_scores_gemma":[0.00001897782,0.00003236427,0.000396578,0.0000263872,0.00002473105,0.00006050064,0.00002314607,0.848289,0.0005666396,0.1465478,0.003991462,0.00002247935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005498639,0.001125866,0.99002,0.0007081368,0.0001556409,0.00004921102,0.000373012,0.0004157817,0.00165359],"genre_scores_gemma":[0.5923413,0.002766379,0.3845335,0.0009638503,0.0005431659,0.0005571981,0.002354622,0.0002900953,0.01564993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005484261,"threshold_uncertainty_score":0.0177713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04979499023872413,"score_gpt":0.2534673949242112,"score_spread":0.2036724046854871,"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."}}