{"id":"W3112163844","doi":"10.21203/rs.3.rs-76131/v1","title":"Breast Cancer Detection From a Urine Sample by Dog Sniffing","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Breast cancer; Medicine; Urine; Sniffing; Cancer; Internal medicine; Oncology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006174339,0.0003354515,0.0002636794,0.0003531407,0.0001833082,0.0002496651,0.0001805131,0.0004568702,0.001560299],"category_scores_gemma":[0.0008820527,0.0001391784,0.0001787559,0.0001593913,0.0002242269,0.0001648394,0.0002186059,0.0001997943,0.0006660904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126865,"about_ca_system_score_gemma":0.0001675187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005996996,"about_ca_topic_score_gemma":0.0008930588,"domain_scores_codex":[0.9996102,0.000107298,0.00002479736,0.0001038334,0.0001135083,0.00004025638],"domain_scores_gemma":[0.9995987,0.000109253,0.00007883996,0.00003296829,0.0001294579,0.00005085007],"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":[0.001140472,0.0006926528,0.145082,0.0002010672,0.0001085438,0.0002669187,0.0002531458,0.0001465655,0.8202142,0.00004898964,0.0006081131,0.03123744],"study_design_scores_gemma":[0.00006917463,0.007298484,0.3575611,0.00004716036,0.0002174563,0.003287574,0.000515733,0.004526596,0.6223614,0.000109671,0.003968643,0.00003723774],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918085,0.0005974101,0.006003623,0.0000826976,0.00004983312,0.00007072792,0.0002192852,0.0001254218,0.001042534],"genre_scores_gemma":[0.9836105,0.0003427117,0.01298309,0.0002075419,0.000025934,0.00005002362,0.0004467229,0.00001667092,0.002316698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001560299,"threshold_uncertainty_score":0.005219698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06923209976049126,"score_gpt":0.3846840947002756,"score_spread":0.3154519949397843,"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."}}