{"id":"W4233608880","doi":"10.3410/f.3163956.2911057","title":"Faculty Opinions recommendation of Deciphering the splicing code.","year":2010,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RNA splicing; Code (set theory); Computational biology; Computer science; Information retrieval; Genetics; Biology; Programming language; Gene","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002136965,0.0004600173,0.0006248855,0.000146694,0.0003465952,0.0001350279,0.002388076,0.001034864,0.0001981997],"category_scores_gemma":[0.008512919,0.0002541657,0.0006846602,0.0009637452,0.0007644765,0.00002569902,0.001129836,0.001310774,0.00003208798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003955988,"about_ca_system_score_gemma":0.0005431327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006671341,"about_ca_topic_score_gemma":0.00006264732,"domain_scores_codex":[0.9957209,0.0002715554,0.001489619,0.0005487374,0.001509847,0.0004592928],"domain_scores_gemma":[0.9923402,0.0001070059,0.001127179,0.001853868,0.004252509,0.0003192127],"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":[0.00001312274,0.0001548307,0.000004305196,0.003457332,0.000132904,7.655732e-8,0.00005931637,1.616818e-7,0.0001224257,0.00002656017,0.9904994,0.005529602],"study_design_scores_gemma":[0.0004417259,0.000174942,0.0004218869,0.002842739,0.00009767304,0.00002567759,0.00005110434,0.00002712823,0.0005320106,0.00001076971,0.995101,0.0002732952],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004509374,0.000812906,0.0001734241,0.09876951,0.0008800577,0.001045994,0.8982047,0.00001146481,0.00009739211],"genre_scores_gemma":[0.00002209615,0.001409519,0.001205683,0.003721215,0.0006361229,0.0001302521,0.9920009,0.00002612862,0.0008481064],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09504829,"threshold_uncertainty_score":0.9999911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03268398659187138,"score_gpt":0.3641107017588843,"score_spread":0.3314267151670129,"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."}}