{"id":"W4231186245","doi":"10.1515/iupac.79.1028","title":"Cloning Vector","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":"Cloning (programming); Vector (molecular biology); Computer science; Biology; Computational biology; Genetics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002194969,0.002428935,0.002782142,0.003431161,0.001328842,0.003741771,0.003962029,0.002248077,0.1338958],"category_scores_gemma":[0.008669209,0.001172504,0.002031041,0.006569093,0.0005034155,0.002362044,0.001897251,0.002917073,0.2195163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001269541,"about_ca_system_score_gemma":0.003304322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009361262,"about_ca_topic_score_gemma":0.01551455,"domain_scores_codex":[0.9976808,0.0003934337,0.0003508152,0.0009498592,0.0003924751,0.0002325387],"domain_scores_gemma":[0.997619,0.000751908,0.0002175814,0.0007672277,0.0004594836,0.0001846583],"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":[0.0003704215,0.00006480697,0.001046845,0.001676193,0.0001049699,0.00004151045,0.00004326377,0.0005079242,0.001225885,0.001396521,0.9826936,0.0108281],"study_design_scores_gemma":[0.0004003317,0.00005754435,0.002210833,0.0002430562,0.0001110492,0.0001125241,0.00005151557,0.0006434869,0.001383801,0.002723413,0.9920195,0.00004293275],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001544092,0.0001557234,0.0005503683,0.00006942483,0.00004904046,0.00005131452,0.9967001,0.001134654,0.001135018],"genre_scores_gemma":[0.0002207247,0.0001162155,0.001036491,0.00008931934,0.000008254392,0.0001785419,0.997267,0.0001879342,0.0008954305],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1338958,"threshold_uncertainty_score":0.4479262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328547552373333,"score_gpt":0.3868243507383583,"score_spread":0.373538875214625,"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."}}