{"id":"W4248383013","doi":"10.1515/iupac.83.0379","title":"Heterogeneous Assay","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Glossary; Context (archaeology); Field (mathematics); Process (computing); Computer science; Multidisciplinary approach; Data science; Component (thermodynamics); Management science; Engineering; Sociology; Biology; Linguistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002964938,0.002877123,0.002080511,0.004130581,0.001130609,0.004227132,0.004363652,0.002049968,0.05525582],"category_scores_gemma":[0.01148038,0.001072144,0.002448979,0.006077473,0.0006710066,0.002831223,0.003241029,0.003234203,0.09729674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002228082,"about_ca_system_score_gemma":0.002859422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01366375,"about_ca_topic_score_gemma":0.0241242,"domain_scores_codex":[0.9960258,0.0006049489,0.0006455812,0.001197626,0.0011623,0.0003637384],"domain_scores_gemma":[0.9940698,0.001487109,0.0006400138,0.002314822,0.001161746,0.00032651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003161182,0.00007047587,0.003251197,0.001591742,0.00008346151,0.00005249804,0.00004305369,0.0007841477,0.0005704587,0.00324961,0.975697,0.01429022],"study_design_scores_gemma":[0.000168713,0.00003168279,0.005021336,0.0003802611,0.0000507681,0.0001637354,0.00005820436,0.0008705533,0.001345875,0.003539692,0.9883162,0.00005295536],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004530907,0.0003350773,0.001320292,0.0001027868,0.00005810639,0.00007119309,0.9922757,0.00230107,0.003082661],"genre_scores_gemma":[0.0006713049,0.000152311,0.001474712,0.0001007791,0.000007754239,0.0001386944,0.9961482,0.0001941793,0.00111201],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05525582,"threshold_uncertainty_score":0.1848491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01434207026785708,"score_gpt":0.3844931669793076,"score_spread":0.3701510967114506,"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."}}