{"id":"W2997520716","doi":"10.1002/mds3.10058","title":"Quantum cytosensor for early detection of cancer","year":2019,"lang":"en","type":"article","venue":"Medical Devices & Sensors","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantum dot; Nanotechnology; Cancer; Graphene; Cancer detection; Cancer cell; Quantum; Biomarker; Computer science; Materials science; Computational biology; Biology; Physics; Biochemistry; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002909495,0.0002583082,0.0001752944,0.0004309462,0.0001494259,0.0002283531,0.0003303709,0.0006668316,0.001242485],"category_scores_gemma":[0.0003487036,0.000137808,0.0001422478,0.0002087678,0.0003023559,0.000289807,0.0002867715,0.0005507517,0.0003900855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003550779,"about_ca_system_score_gemma":0.0002004543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004058913,"about_ca_topic_score_gemma":0.000732294,"domain_scores_codex":[0.9996889,0.00006016071,0.00001114457,0.0000697319,0.0001471987,0.00002290523],"domain_scores_gemma":[0.999778,0.0001022088,0.00002702206,0.00001733828,0.00005644555,0.00001895627],"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":[0.00001803888,0.00001234807,0.0001497577,0.0000566435,0.000004710388,0.00002669751,0.00001137136,0.0003589343,0.9938782,0.0004963244,0.0002033339,0.00478373],"study_design_scores_gemma":[0.000005782193,0.0001068589,0.0007009932,0.000007757882,0.000007292639,0.00007482604,0.00001518216,0.01152644,0.9831817,0.0003951391,0.003965671,0.00001242826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5876742,0.01401523,0.3768373,0.001630766,0.000767866,0.0002523778,0.001064997,0.003266653,0.01449057],"genre_scores_gemma":[0.9107457,0.002139158,0.0813987,0.0004205657,0.00005933731,0.00008328763,0.0002631991,0.00003154075,0.004858505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001242485,"threshold_uncertainty_score":0.00415653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266195068051759,"score_gpt":0.2891470860139316,"score_spread":0.2764851353334141,"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."}}