{"id":"W3112099004","doi":"10.1007/978-3-030-64559-5_3","title":"Image Categorization Using Agglomerative Clustering Based Smoothed Dirichlet Mixtures","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Hierarchical clustering; Cluster analysis; Artificial intelligence; Categorization; Pattern recognition (psychology); Robustness (evolution); Dirichlet distribution; Metric (unit); Divergence (linguistics); Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004674584,0.0005265769,0.0004905517,0.0006439248,0.0003163365,0.000884075,0.002295814,0.0002748105,0.00002045293],"category_scores_gemma":[0.0001339675,0.0004807278,0.0001346039,0.001025981,0.0005355196,0.0008110297,0.0007912585,0.0006376858,0.00002208655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003871318,"about_ca_system_score_gemma":0.0006349063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002017615,"about_ca_topic_score_gemma":0.0000132308,"domain_scores_codex":[0.9966291,0.00006314244,0.0005278384,0.001427418,0.0008899692,0.0004625792],"domain_scores_gemma":[0.997857,0.0002401546,0.0004092653,0.0009325005,0.0003943535,0.0001667515],"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":[0.0000535602,0.000134052,0.00005819509,0.0004486273,0.00005456358,0.0003878779,0.002741922,0.02340665,0.1664758,0.03627321,0.00007085763,0.7698947],"study_design_scores_gemma":[0.0001572363,0.00009347289,0.0000191778,0.0001883207,0.000008317103,0.00001576154,1.205651e-7,0.9003224,0.06883077,0.02947285,0.0003402703,0.0005512774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000005288292,0.0001855344,0.9957561,0.001502962,0.0006208707,0.000455221,0.000007594852,0.0003993995,0.00106696],"genre_scores_gemma":[0.07078322,0.00002280324,0.9262636,0.002406515,0.0003619743,0.00001056251,0.00001621458,0.00004871731,0.00008641315],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8769158,"threshold_uncertainty_score":0.9997644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02548097859121622,"score_gpt":0.267288238034797,"score_spread":0.2418072594435807,"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."}}