{"id":"W75994072","doi":"10.1007/978-1-4615-1141-0_3","title":"Discovering Patterns With and Within Images","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Knowledge extraction; Data mining; Process (computing); Data science; Business intelligence; Business process discovery; K-optimal pattern discovery; Association rule learning; Information retrieval; Business process; Work in process; Business process modeling; Engineering","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":[0.0003392936,0.0008209668,0.0007173117,0.002639252,0.0004268592,0.002121109,0.001550093,0.0007325732,0.007856826],"category_scores_gemma":[0.00185807,0.0004714655,0.0007905847,0.003669018,0.00096034,0.003777342,0.0008751387,0.0007838865,0.005060987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004686994,"about_ca_system_score_gemma":0.0004681582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413388,"about_ca_topic_score_gemma":0.002503723,"domain_scores_codex":[0.9996463,0.00002986987,0.00002444452,0.0001426348,0.0001371289,0.00001956778],"domain_scores_gemma":[0.9994691,0.0002460942,0.0000543747,0.0001284039,0.00008189167,0.00002018685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004297234,0.00003941333,0.001705211,0.0005608507,0.00004527008,0.0001737108,0.0002343566,0.001022938,0.01456006,0.02095782,0.01525083,0.9454066],"study_design_scores_gemma":[0.00004029213,0.0001781036,0.01502901,0.0007360324,0.0004235673,0.007929544,0.001454899,0.1134204,0.09402765,0.3976754,0.368977,0.0001080653],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03180411,0.01356828,0.8992183,0.002041889,0.0003391925,0.0002320538,0.001668413,0.002756533,0.04837117],"genre_scores_gemma":[0.0802503,0.01306053,0.860112,0.0004166933,0.0003195453,0.0001322353,0.00323446,0.0003787168,0.04209547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007856826,"threshold_uncertainty_score":0.02628368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01490180493231034,"score_gpt":0.2187484374131523,"score_spread":0.203846632480842,"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."}}