{"id":"W4389145614","doi":"10.3390/engproc2023051038","title":"Diagnostic Biomarker for Breast Cancer Applying Rayleigh Low-Rank Embedding Thermography","year":2023,"lang":"en","type":"article","venue":"","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Basis (linear algebra); Breast cancer; Computer science; Embedding; Thermography; Rank (graph theory); Pattern recognition (psychology); Artificial intelligence; Machine learning; Mathematics; Cancer; Medicine; Physics; Internal medicine; Geometry; Optics","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":[0.001429286,0.001039638,0.0007821442,0.001300962,0.0002147458,0.0008095526,0.0004017588,0.0006353983,0.00148341],"category_scores_gemma":[0.004808212,0.0002008812,0.0006571703,0.0006525727,0.0003369057,0.000730692,0.000676506,0.0006879862,0.00111588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002345402,"about_ca_system_score_gemma":0.0005493265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001273927,"about_ca_topic_score_gemma":0.001844658,"domain_scores_codex":[0.9992468,0.000312821,0.00004501974,0.0001484334,0.0001727426,0.00007423533],"domain_scores_gemma":[0.9988294,0.0005684759,0.0001669826,0.0001117482,0.0002722385,0.00005124321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007276286,0.0002831907,0.02782629,0.0004123665,0.0002915344,0.0003261075,0.0001578998,0.1249449,0.05298086,0.004749099,0.0081645,0.7791357],"study_design_scores_gemma":[0.00002634175,0.0002556806,0.00781132,0.00006315606,0.0001138048,0.0004761463,0.00007344287,0.9606788,0.01986649,0.006225755,0.004348442,0.0000606489],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1305,0.00426031,0.8584738,0.001014479,0.0002388095,0.0001876728,0.0007831389,0.001591159,0.002950709],"genre_scores_gemma":[0.8125077,0.001937315,0.1805426,0.0002343851,0.0002528166,0.0001480668,0.001265452,0.0001077361,0.003004001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00148341,"threshold_uncertainty_score":0.007558882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064259232482248,"score_gpt":0.3197623963669435,"score_spread":0.299119804042121,"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."}}