{"id":"W2789871865","doi":"10.1109/ipta.2017.8310106","title":"A new latent generalized dirichlet allocation model for image classification","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Latent Dirichlet allocation; Inference; Robustness (evolution); Computer science; Generative grammar; Topic model; Hierarchical Dirichlet process; Artificial intelligence; Machine learning; Prior probability; Generative model; Pattern recognition (psychology); Data mining; Bayesian probability","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":[0.0002459249,0.0001110345,0.0001137046,0.00005427782,0.0003753862,0.0006414615,0.001145242,0.00006639426,0.00001188624],"category_scores_gemma":[0.00008811346,0.00009354865,0.00008054136,0.00006284644,0.00003373006,0.001043129,0.0001212978,0.00004792568,0.00003803863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004865719,"about_ca_system_score_gemma":0.0001228879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006220956,"about_ca_topic_score_gemma":0.000008546363,"domain_scores_codex":[0.9990897,0.0000151963,0.0002152712,0.0003490975,0.000151808,0.0001789372],"domain_scores_gemma":[0.9983317,0.0000166842,0.0002056071,0.001118401,0.0002350504,0.00009261182],"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":[0.00001096546,0.00004012728,0.00005310379,0.00001072409,0.000008663289,2.78696e-7,0.0001136847,0.000006318358,0.0831762,0.7872258,0.01676457,0.1125896],"study_design_scores_gemma":[0.0003060773,0.00002001278,0.001750277,0.000004381873,0.000005668153,0.000001229471,0.000001952008,0.8950874,0.06532938,0.03504524,0.002315946,0.0001324909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002041135,0.00001889788,0.9851938,0.01082195,0.00009227946,0.0003920778,0.000002361854,0.0003712352,0.002903288],"genre_scores_gemma":[0.1912284,0.00004720801,0.7899193,0.0003119897,0.00006512061,0.00009980326,0.00001236976,0.000009762978,0.01830609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.895081,"threshold_uncertainty_score":0.6185628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07883892770083747,"score_gpt":0.3287784068994662,"score_spread":0.2499394791986287,"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."}}