{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000460561,0.0001473862,0.0001871759,0.0001735655,0.000108062,0.00006501253,0.0007269547,0.000177404,0.00002158712],"category_scores_gemma":[0.0000403084,0.0001172387,0.000103077,0.0005854773,0.00005760868,0.0002134303,0.00005399441,0.0001388989,5.535521e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004495075,"about_ca_system_score_gemma":0.0001613054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002869792,"about_ca_topic_score_gemma":0.000001034687,"domain_scores_codex":[0.9988281,0.00007037701,0.0002508212,0.0003821432,0.0002338968,0.0002347003],"domain_scores_gemma":[0.9985523,0.00004324564,0.0001574936,0.000653216,0.0004899382,0.000103809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003844007,0.0008231095,0.0003049368,0.0004799085,0.00002042889,0.000008286913,0.001519969,0.00001469507,0.7469898,0.221012,0.0005009908,0.02794148],"study_design_scores_gemma":[0.0001196252,0.0003407067,0.0002798148,0.00004980654,0.000004098142,0.000005883496,0.00001629698,0.2093698,0.7880468,0.001238036,0.0003897071,0.0001393702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002432093,0.000127506,0.9954277,0.0004723508,0.00007916051,0.0007351174,0.00001763926,0.0004192349,0.0002891721],"genre_scores_gemma":[0.5180706,0.00000153901,0.4815809,0.0002531701,0.00001886337,0.00001771296,0.000005125929,0.000008274221,0.00004386082],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5156385,"threshold_uncertainty_score":0.4780857,"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."}}