{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004791899,0.0009315064,0.0004977071,0.000948991,0.0003847362,0.0006429101,0.001048287,0.0008417559,0.002061061],"category_scores_gemma":[0.001672951,0.0002893757,0.0008551585,0.001202445,0.0004314818,0.001948696,0.001199047,0.0006442441,0.001028701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008830955,"about_ca_system_score_gemma":0.0006165595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01709698,"about_ca_topic_score_gemma":0.01979385,"domain_scores_codex":[0.9997151,0.00003403478,0.0000178906,0.0001311059,0.00005562474,0.00004612161],"domain_scores_gemma":[0.9996402,0.00008516114,0.00003732939,0.0001062318,0.0001056009,0.00002549881],"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":[0.0003645091,0.0002290282,0.007598149,0.0002225511,0.0001313913,0.0002470921,0.0004729296,0.09702206,0.03854056,0.009862797,0.01442233,0.8308866],"study_design_scores_gemma":[0.000009485221,0.00003947226,0.001292501,0.00001099061,0.00003740389,0.00006588327,0.00004385203,0.9786789,0.008837966,0.00738104,0.003589767,0.00001269936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09603797,0.0007347085,0.8886673,0.0003524978,0.0001136758,0.0001818746,0.001007351,0.006494136,0.006410487],"genre_scores_gemma":[0.7390341,0.0004128366,0.2470316,0.0004261242,0.0001024827,0.0002210746,0.003300841,0.0002856623,0.009185308],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01709698,"threshold_uncertainty_score":0.03399497,"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."}}