{"id":"W2137713158","doi":"10.1145/1980422.1980446","title":"An efficient shape based feature for retrieval of healthcare literatures using CBIR technique","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Image retrieval; Precision and recall; Information retrieval; Feature extraction; Feature (linguistics); Content-based image retrieval; Fourier transform; Artificial intelligence; Pattern recognition (psychology); Data mining; Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008730619,0.0005449942,0.0009497631,0.007999058,0.0005351994,0.001005661,0.0006734215,0.0008509937,0.002199926],"category_scores_gemma":[0.004419257,0.0002079525,0.0008903515,0.005306464,0.0003826228,0.001153798,0.0006501426,0.0004287355,0.001765001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004593814,"about_ca_system_score_gemma":0.001085458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003971912,"about_ca_topic_score_gemma":0.004305747,"domain_scores_codex":[0.9991794,0.000107342,0.0001004829,0.0001106415,0.0004314024,0.00007067897],"domain_scores_gemma":[0.9985533,0.0004718188,0.0001104892,0.0001887432,0.0006249486,0.00005062834],"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":[0.0003895751,0.0001208789,0.002066765,0.000435131,0.00006332441,0.000437177,0.0001545482,0.002404374,0.170609,0.001921057,0.007789136,0.813609],"study_design_scores_gemma":[0.0003103759,0.001065103,0.05312946,0.0002215693,0.0004989019,0.0114299,0.0009236514,0.5269372,0.3378963,0.009254766,0.05797782,0.0003550212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1115339,0.004475564,0.8673788,0.0008682406,0.000332899,0.0005755209,0.002527104,0.006461505,0.005846409],"genre_scores_gemma":[0.2610912,0.001109526,0.7318368,0.0002194986,0.0001905696,0.0003102868,0.002486957,0.0001701927,0.002585076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007999058,"threshold_uncertainty_score":0.007897556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05100542798183744,"score_gpt":0.3131484920201059,"score_spread":0.2621430640382685,"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."}}