{"id":"W4231723765","doi":"10.1515/iupac.88.1047","title":"Messenger Rna (mRNA)","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Biology; Data science; Linguistics; Data mining; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005600918,0.0003815715,0.0004324715,0.0001011018,0.000264848,0.0001453299,0.0009254994,0.0005528053,0.0005279672],"category_scores_gemma":[0.0009980808,0.0003366979,0.0002220574,0.0000453613,0.0001911415,0.000004564256,0.0004651067,0.0005205508,0.000005361884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007261895,"about_ca_system_score_gemma":0.0009390755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001808064,"about_ca_topic_score_gemma":0.001180845,"domain_scores_codex":[0.9976589,0.00007827143,0.0002767092,0.0006178091,0.0008068865,0.0005614295],"domain_scores_gemma":[0.9973855,0.00001407181,0.0002250007,0.001686449,0.0004241111,0.0002648605],"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":[0.0001841846,0.00008899914,0.000009372077,0.00008738125,0.0001511346,0.00008801188,0.000001162137,0.000001757191,0.02464692,7.151729e-7,0.9729604,0.001780013],"study_design_scores_gemma":[0.0006408853,0.000338535,0.00004940991,0.0001048393,0.00004521641,0.00001902306,0.000008533611,0.000005463557,0.003314612,0.000007955913,0.9950845,0.0003810494],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007320069,0.002196511,0.00003200357,0.0003437704,0.0004019694,0.0002219193,0.9893379,0.00001240523,0.0001334239],"genre_scores_gemma":[0.0003295168,0.003271435,0.00005181285,0.000203525,0.001686337,0.00001579819,0.9886253,0.00004528406,0.005770953],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02212412,"threshold_uncertainty_score":0.9999085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620743685137216,"score_gpt":0.4272218344741934,"score_spread":0.4110143976228213,"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."}}