{"id":"W4252899169","doi":"10.1101/gr.gr1644r","title":"A Method For Parallel, Automated, Thermal Cycling of Submicroliter Samples","year":2001,"lang":"en","type":"article","venue":"Genome Research","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Human Genome Research Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; DNA sequencing; Temperature cycling; Computational biology; Fraction (chemistry); Cycling; Sample (material); Biochemical engineering; Reliability engineering; Computer science; DNA; Process engineering; Thermal; Chromatography; Genetics; Engineering; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001977878,0.0001053528,0.0001781465,0.0003556973,0.00007865998,0.00002243589,0.0002342614,0.000098708,0.00007407869],"category_scores_gemma":[0.00004362776,0.0001003909,0.00004799566,0.0007794067,0.00007931238,0.00007389485,0.00005745449,0.0002119256,0.0000146749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001025582,"about_ca_system_score_gemma":0.00003613072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002417606,"about_ca_topic_score_gemma":3.331901e-7,"domain_scores_codex":[0.9988033,0.00004970829,0.0003224141,0.0001645541,0.0002109644,0.0004491158],"domain_scores_gemma":[0.9991307,0.0001547295,0.00002781561,0.0002377259,0.0004203212,0.00002865769],"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":[0.00003547654,0.00001629083,0.0001373498,0.0001179006,0.00003993186,0.000002352045,0.0003023988,0.0002276191,0.9781021,0.003732006,0.001549307,0.01573726],"study_design_scores_gemma":[0.0007431168,0.0001616285,0.003258586,0.00005519022,0.00001012392,0.00003069794,0.0002699361,0.02649285,0.7897401,0.003998243,0.1749067,0.0003327795],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1372819,0.000752637,0.8576476,0.00007967601,0.00003550955,0.0004919378,0.00004399435,0.0003866575,0.003280095],"genre_scores_gemma":[0.854575,0.00009954011,0.144529,0.00002823097,0.0001327452,0.0001659045,0.0001263665,0.00005289785,0.000290334],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7172931,"threshold_uncertainty_score":0.4093822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08948869781072281,"score_gpt":0.3932200929707012,"score_spread":0.3037313951599783,"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."}}