{"id":"W2808312973","doi":"10.1038/s41598-018-27500-3","title":"In planta proximity dependent biotin identification (BioID)","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Biotin and Related Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Toronto; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Ontario Genomics; Genome Canada","keywords":"Proteome; Biotin; Computational biology; Identification (biology); Proteomics; Arabidopsis thaliana; Arabidopsis; Biology; Cell biology; Bioinformatics; Biochemistry; Botany; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007513218,0.0001093773,0.00009835607,0.00009086885,0.0002273433,0.00008941501,0.0001216836,0.0001552739,0.00003488174],"category_scores_gemma":[0.0001118568,0.00009285001,0.00004774348,0.0002256994,0.0003699662,0.00000853344,0.0001304248,0.00008346968,0.00006009332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001740739,"about_ca_system_score_gemma":0.00006801011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002452647,"about_ca_topic_score_gemma":0.0002606713,"domain_scores_codex":[0.9984613,0.00003916864,0.0003626221,0.0006865031,0.0002073236,0.0002430991],"domain_scores_gemma":[0.9990753,0.000002854343,0.0001639201,0.0005801392,0.0001243997,0.00005340741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000173366,0.00008657862,0.008985363,0.00001325295,0.00001941397,0.00005302402,0.0001353815,0.000002093031,0.9636195,0.00002873186,0.02644658,0.0005927442],"study_design_scores_gemma":[0.0001253421,0.00004465871,0.006905498,0.00001684644,0.000007753792,0.0001465884,0.00007177069,0.00001454594,0.9344409,0.000624574,0.0574584,0.0001431852],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885639,0.0005587202,0.0001569366,0.0002551518,0.006909995,0.0002509113,0.000003804833,0.00001844652,0.00328211],"genre_scores_gemma":[0.9891325,0.00004501714,0.0001551047,0.00005252772,0.0002160507,0.000009704289,0.00007200414,0.000009243669,0.01030781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03101182,"threshold_uncertainty_score":0.3786314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021038432504952,"score_gpt":0.2545490721283011,"score_spread":0.2443386878032516,"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."}}