{"id":"W2791167821","doi":"10.1162/neco_a_01077","title":"Facet Annotation by Extending CNN with a Matching Strategy","year":2018,"lang":"en","type":"article","venue":"Neural Computation","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Facet (psychology); Matching (statistics); Mathematics; Representation (politics); Convolutional neural network; Pattern recognition (psychology); Computer science; Similarity (geometry); Artificial intelligence; Image (mathematics); Statistics; Psychology","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":[0.0001091095,0.0001030063,0.00008491569,0.00006523801,0.0001698007,0.0002477389,0.0002202652,0.00002976531,0.000004400784],"category_scores_gemma":[0.000005684573,0.00009130891,0.00001608674,0.0002275324,0.00002433151,0.0007966866,0.0000491453,0.0000921175,0.00002630254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003306967,"about_ca_system_score_gemma":0.00002160338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005023714,"about_ca_topic_score_gemma":0.000009926969,"domain_scores_codex":[0.9990514,0.00005485166,0.0001584739,0.0003136896,0.0002369067,0.0001846589],"domain_scores_gemma":[0.9995804,0.00003019814,0.00009416704,0.0001491317,0.00009941762,0.00004671315],"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":[0.0000269667,0.00006136291,0.000663713,0.00003736823,0.0000218473,0.00002123476,0.005024265,0.2530683,0.03261324,0.01870783,0.001196638,0.6885573],"study_design_scores_gemma":[0.0002427277,0.0001949766,0.001918468,0.00001522726,0.000002638474,0.00001981379,0.00005845517,0.9920565,0.001605745,0.003707876,0.00005006132,0.0001274758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4334113,0.00001286434,0.565752,0.0001958658,0.0001139693,0.00006960523,4.999932e-7,0.000128365,0.0003154921],"genre_scores_gemma":[0.9500076,5.623587e-7,0.04961814,0.0002125281,0.00009272719,0.000004420233,0.000009649446,0.000007883943,0.00004652041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7389883,"threshold_uncertainty_score":0.3723469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02702873608636515,"score_gpt":0.2830968403786714,"score_spread":0.2560681042923063,"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."}}