{"id":"W2702636311","doi":"","title":"Chip-Based Sensors for Disease Diagnosis","year":2010,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Genome Canada; University of Patras; Canadian Institutes of Health Research; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Chip; Lab-on-a-chip; Disease; Computer science; Data science; Embedded system; Medicine; Nanotechnology; Telecommunications; Pathology; Materials science; Microfluidics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0009056078,0.0008721033,0.001035005,0.0007424339,0.0002042684,0.0008475108,0.001460673,0.001797329,0.003514321],"category_scores_gemma":[0.0007829238,0.0004950268,0.0006521748,0.0005123236,0.0003641972,0.000773742,0.0008721872,0.001258783,0.003045685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006196237,"about_ca_system_score_gemma":0.000438419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005066765,"about_ca_topic_score_gemma":0.0008361664,"domain_scores_codex":[0.9988004,0.0002337215,0.00005600176,0.0003241073,0.0004903292,0.00009548504],"domain_scores_gemma":[0.9996938,0.0001030903,0.00003456166,0.00003567449,0.0001102923,0.00002271703],"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":[0.0002155366,0.0001396829,0.0009441632,0.002327793,0.0002361736,0.0002539765,0.00009813728,0.002633823,0.7341787,0.01220419,0.02697571,0.2197922],"study_design_scores_gemma":[0.00007503059,0.0005582036,0.0021027,0.0002377859,0.0001951729,0.0009417351,0.00008170275,0.03246846,0.66614,0.006011307,0.2910479,0.0001400025],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04274425,0.2426769,0.6417791,0.006385987,0.005420828,0.0008472691,0.004683857,0.007930356,0.04753139],"genre_scores_gemma":[0.3754584,0.08382082,0.4795116,0.008803533,0.001582793,0.00175943,0.004564852,0.0002496528,0.0442489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003514321,"threshold_uncertainty_score":0.0117566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020185751186408,"score_gpt":0.3369439505559311,"score_spread":0.326742093044067,"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."}}