{"id":"W7072010801","doi":"","title":"Visualization and grading of regional ischemia in pig hearts in vivo using near-infrared and thermal imaging","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Visualization; Grading (engineering); Ischemia; In vivo; Medical imaging","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005873378,0.00009937952,0.0002238251,0.0003034151,0.00003188382,0.000007307891,0.00002612215,0.00005896846,0.00005912309],"category_scores_gemma":[0.00006699246,0.00009548524,0.00002013355,0.0003057342,0.0001840046,0.00009655235,0.00002268254,0.000146096,1.131716e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003915405,"about_ca_system_score_gemma":0.00003743801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005280628,"about_ca_topic_score_gemma":0.000008037054,"domain_scores_codex":[0.9991039,0.00002897346,0.0002926783,0.0001651259,0.0001978108,0.0002114827],"domain_scores_gemma":[0.9996358,0.00007857919,0.00007123057,0.000105953,0.00003852774,0.00006992128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001548678,0.00003379957,0.3790583,0.00008692983,0.000007861645,0.00006644926,0.002773944,0.000003045209,0.6147581,0.0003456487,0.00006779235,0.002643226],"study_design_scores_gemma":[0.007651529,0.0001904948,0.7452677,0.002363228,0.00007527153,0.0004781741,0.002969827,0.04150199,0.1960464,0.002601077,0.0004955465,0.0003587421],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995223,0.0003232908,0.0003265189,0.0001421494,0.00003633637,0.0001870786,9.261681e-7,0.0000142692,0.003746375],"genre_scores_gemma":[0.9956391,0.00002526296,0.003938453,0.0002969706,0.00006243712,0.000001635381,0.000003616181,0.00001666396,0.00001590156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4187117,"threshold_uncertainty_score":0.3893775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01667233395794232,"score_gpt":0.2963298608710689,"score_spread":0.2796575269131266,"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."}}