{"id":"W1983312708","doi":"10.1117/12.525865","title":"A knowledge-capturing approach for a cooperative intelligent image analysis system","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Task (project management); Knowledge base; Image processing; Plan (archaeology); Expert system; Knowledge-based systems; Interface (matter); Image (mathematics); Task analysis; User interface; Human–computer interaction; Artificial intelligence; Engineering; Systems 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":[0.00228528,0.0007227345,0.0006176388,0.001029459,0.001144419,0.003551615,0.00382621,0.001765302,0.003623937],"category_scores_gemma":[0.003690335,0.0006513976,0.001071872,0.0008296529,0.002099526,0.004332185,0.003135083,0.002186022,0.00120146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00165992,"about_ca_system_score_gemma":0.00228424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009517566,"about_ca_topic_score_gemma":0.01094791,"domain_scores_codex":[0.9982186,0.0004300525,0.0001213776,0.0004905266,0.0005750176,0.0001644325],"domain_scores_gemma":[0.9986395,0.0003859604,0.00009358231,0.0004828282,0.0002706655,0.0001273608],"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":[0.0002811302,0.0004926663,0.002007366,0.0004934283,0.0001956734,0.001194112,0.007193924,0.1292021,0.05763127,0.3365459,0.01167162,0.4530908],"study_design_scores_gemma":[0.00005123801,0.0001145012,0.0006376398,0.00009305601,0.0001560385,0.0003180996,0.0005594276,0.7841964,0.01909215,0.1352616,0.05943754,0.00008240595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003482223,0.00004939784,0.9915709,0.0002734456,0.000006543924,0.0001323123,0.00003699298,0.001210563,0.003237509],"genre_scores_gemma":[0.09825294,0.00009915429,0.8966599,0.0002070479,0.0000170763,0.0003147816,0.0002063054,0.000109599,0.004133264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009517566,"threshold_uncertainty_score":0.01892436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685727830803404,"score_gpt":0.247362968659907,"score_spread":0.230505690351873,"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."}}