{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003355473,0.0001073489,0.0002399533,0.00005510087,0.00005216181,0.00001980289,0.0001971172,0.0001165994,0.001484914],"category_scores_gemma":[0.0001153553,0.00008643544,0.00008755589,0.0001561473,0.00009807872,0.00006464472,0.00003184731,0.00008745321,0.0003279414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002587318,"about_ca_system_score_gemma":0.00006374055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001159668,"about_ca_topic_score_gemma":0.0005149557,"domain_scores_codex":[0.9986634,0.00004118653,0.0003225511,0.0002759446,0.0004542922,0.0002425706],"domain_scores_gemma":[0.999119,0.0002367945,0.0001452497,0.0002481555,0.000105789,0.0001449551],"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":[0.00008009451,0.00006221792,0.003743343,0.0001456309,0.0000136321,0.000001102833,0.0002499573,0.00002391613,0.9905369,0.00221236,0.0001910822,0.002739723],"study_design_scores_gemma":[0.001395714,0.0002953649,0.06197705,0.0001693523,0.00008244176,0.000009132583,0.0003701612,0.02052076,0.8511295,0.0005681775,0.06309013,0.0003921948],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972548,0.0001761864,0.000346071,0.0006954024,0.0006470042,0.0004080219,0.00004224911,0.00006782042,0.0003624365],"genre_scores_gemma":[0.9990116,0.00003812978,0.0001035376,0.0002501577,0.000153861,0.00007628262,0.000003327649,0.00001708757,0.0003459952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1394074,"threshold_uncertainty_score":0.9994279,"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."}}