{"id":"W2963651181","doi":"10.3390/biom9080299","title":"Small Genomes and Big Data: Adaptation of Plastid Genomics to the High-Throughput Era","year":2019,"lang":"en","type":"review","venue":"Biomolecules","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Plastid; Genome; Biology; Tree of life (biology); Adaptation (eye); Genomics; Lineage (genetic); Computational biology; Evolutionary biology; Comparative genomics; DNA sequencing; Phylogenetics; Gene; Genetics; Chloroplast","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":[0.0001889633,0.0003506464,0.0006887746,0.00007045495,0.0000787856,0.00003625235,0.0006855301,0.0002120987,0.000001219475],"category_scores_gemma":[0.00004947147,0.0002465018,0.0001314528,0.0001133151,0.0001367183,6.063546e-7,0.001095899,0.00008535287,0.00001348625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001488839,"about_ca_system_score_gemma":0.0002569162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001184131,"about_ca_topic_score_gemma":0.0002206779,"domain_scores_codex":[0.9985024,0.00009040677,0.0004182866,0.0006625257,0.0000961184,0.0002302513],"domain_scores_gemma":[0.9984825,0.00005117595,0.0002705072,0.001066657,0.00006904318,0.00006014984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001572981,0.00002527219,0.000008383391,0.001641991,0.0004922977,0.000001357632,0.00007995892,0.00003253836,0.003441838,0.00009303924,0.001021916,0.9931457],"study_design_scores_gemma":[0.0001326102,0.0001865854,0.00005107424,0.0002290052,0.0003964593,0.0000140399,0.00005965125,0.000008712075,0.0003068307,0.00000855971,0.998319,0.0002874483],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003094335,0.9926176,0.001371338,0.00006765002,0.0005314918,0.0007049508,0.001549579,0.000002544242,0.00006052512],"genre_scores_gemma":[0.001371954,0.9942383,0.002595872,0.00007265598,0.0005470294,0.00003813608,0.0009504916,0.00005485368,0.0001307688],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9972971,"threshold_uncertainty_score":0.9999987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1291823251254467,"score_gpt":0.2957304631031261,"score_spread":0.1665481379776794,"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."}}