{"id":"W1750301863","doi":"10.1109/icpr.2002.1044648","title":"A trainable hierarchical hidden Markov tree model for color image annotation","year":2003,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bijection; Computer science; Artificial intelligence; Context (archaeology); Multispectral image; Set (abstract data type); Pattern recognition (psychology); Tree (set theory); Hidden Markov model; Markov process; Image (mathematics); Markov chain; Markov model; Theoretical computer science; Machine learning; Mathematics; Geography","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":[0.0003280809,0.00009865015,0.0001102603,0.00007658774,0.00011797,0.0001441882,0.0003590475,0.00005938351,0.00002112665],"category_scores_gemma":[0.0001731567,0.0000834544,0.00007278226,0.0002638441,0.00004541795,0.0004996283,0.00003007025,0.00006774315,0.00001442949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004298595,"about_ca_system_score_gemma":0.0001233645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002263296,"about_ca_topic_score_gemma":0.000001604967,"domain_scores_codex":[0.999076,0.00004276788,0.000186168,0.000294703,0.0001640976,0.0002362833],"domain_scores_gemma":[0.9993122,0.0001165338,0.00004917135,0.0002877865,0.0001621261,0.00007221024],"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":[0.00002035004,0.0001112192,0.000005797936,0.00002258551,0.00000645534,0.000001795684,0.0002898493,0.00000304469,0.02511359,0.834743,0.00549735,0.134185],"study_design_scores_gemma":[0.0003265967,0.00008443552,0.00006614544,0.000002836392,0.000003589457,0.000005009824,0.00002226585,0.8566647,0.06456756,0.07505672,0.003062288,0.0001378449],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004800055,0.00001444495,0.9727293,0.001525658,0.00004167921,0.0003562659,0.000003586458,0.0003293695,0.02451971],"genre_scores_gemma":[0.1516988,0.000006144864,0.8309395,0.0003666556,0.00001387485,0.0001564918,0.000004119351,0.000008357115,0.01680604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8566617,"threshold_uncertainty_score":0.3403172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02423125575090645,"score_gpt":0.2751399174163665,"score_spread":0.25090866166546,"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."}}