{"id":"W4244889751","doi":"10.1515/iupac.79.1320","title":"Gene Amplification","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Chemistry; Biology; Philosophy; Linguistics","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":[0.001659235,0.002311929,0.002340508,0.003707062,0.00130314,0.003847939,0.003401909,0.002458248,0.1228947],"category_scores_gemma":[0.009292621,0.0008399748,0.002311256,0.006059721,0.0004990406,0.001885274,0.001865646,0.00225816,0.1705104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001631744,"about_ca_system_score_gemma":0.003413094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01109193,"about_ca_topic_score_gemma":0.02142152,"domain_scores_codex":[0.9977788,0.00027815,0.0003037215,0.0009607488,0.0004481587,0.0002303661],"domain_scores_gemma":[0.9969607,0.0009915122,0.0003151918,0.000887498,0.0006605724,0.0001845596],"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":[0.0002815727,0.00003740747,0.001888149,0.002410896,0.0000975149,0.00004318359,0.00003336972,0.0004780961,0.0006690176,0.001063367,0.9820732,0.0109242],"study_design_scores_gemma":[0.0003341289,0.00004506217,0.003802216,0.0005804087,0.0001176319,0.0001422039,0.00005016733,0.0004444897,0.0009398116,0.002594732,0.9909054,0.00004379459],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001589737,0.00027149,0.0002177626,0.00007193125,0.00003887181,0.00002417647,0.9974522,0.0005642305,0.001200382],"genre_scores_gemma":[0.000387884,0.000194247,0.0006488035,0.0001473046,0.000009909199,0.0001465284,0.9973942,0.0001141087,0.0009569925],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1228947,"threshold_uncertainty_score":0.4111238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739694146082182,"score_gpt":0.3898238342809257,"score_spread":0.3724268928201038,"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."}}