{"id":"W2000545958","doi":"10.1109/ictai.2013.48","title":"Visual Scenes Categorization Using a Flexible Hierarchical Mixture Model Supporting Users Ontology","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"","keywords":"Categorization; Computer science; Hierarchy; Mixture model; Ontology; Dirichlet distribution; Component (thermodynamics); Hierarchical Dirichlet process; Set (abstract data type); Class (philosophy); Probability density function; Artificial intelligence; Latent Dirichlet allocation; Function (biology); Machine learning; Data mining; Topic model; Mathematics; Statistics","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":[0.0001599872,0.00012111,0.0001376284,0.0001195052,0.0001469211,0.0002052555,0.0004061833,0.0001149462,0.00006485841],"category_scores_gemma":[0.00004457112,0.0000998289,0.00004881382,0.0003677061,0.00005663881,0.0008576976,0.0001370706,0.0001314539,0.00003895522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004742857,"about_ca_system_score_gemma":0.0001354164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001442885,"about_ca_topic_score_gemma":0.000003222005,"domain_scores_codex":[0.9988419,0.00005048226,0.0002760156,0.0003297741,0.0001977965,0.0003040795],"domain_scores_gemma":[0.9993323,0.00003103801,0.0001067255,0.0002504484,0.0001845684,0.00009490121],"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":[0.000009524083,0.0002238976,0.002653539,0.00004921941,0.00002480868,0.000005996988,0.0009055985,0.0007189135,0.4008586,0.4727675,0.001366833,0.1204156],"study_design_scores_gemma":[0.00006770198,0.00002783541,0.0002886533,0.000004203096,0.000002762433,0.00001253197,0.00002067314,0.8837086,0.09598249,0.01969122,0.00006667199,0.0001266809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02166616,0.00001443448,0.9745353,0.001631583,0.00006816245,0.0001949338,3.081424e-7,0.000558098,0.001331013],"genre_scores_gemma":[0.7620074,0.000004788798,0.2364849,0.0004941925,0.00003119069,0.00001589514,0.000004565572,0.000008041542,0.0009491337],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8829896,"threshold_uncertainty_score":0.4070904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02908168093248022,"score_gpt":0.3130250078799808,"score_spread":0.2839433269475006,"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."}}