{"id":"W2044969458","doi":"10.1371/journal.pone.0000325","title":"DNA Barcodes Provide a Quick Preview of Mitochondrial Genome Composition","year":2007,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Genome Canada","keywords":"Genome; Barcode; Mitochondrial DNA; Biology; GC-content; Computational biology; Genetics; DNA; Composition (language); DNA sequencing; DNA barcoding; Evolutionary biology; Gene; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000631134,0.0005124708,0.0005762579,0.002721689,0.0004172589,0.0009201418,0.0004289454,0.0007518819,0.005294215],"category_scores_gemma":[0.003220803,0.0004822476,0.0003839581,0.001637057,0.0004758911,0.00166885,0.0005834128,0.001389488,0.00392003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002864507,"about_ca_system_score_gemma":0.0003667815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006070026,"about_ca_topic_score_gemma":0.001280675,"domain_scores_codex":[0.9995029,0.0000728872,0.00003730241,0.0001162918,0.0002148943,0.00005565405],"domain_scores_gemma":[0.9978683,0.0007376841,0.0005636995,0.0002230095,0.0004601532,0.0001471679],"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":[0.0003295715,0.00004232252,0.005865206,0.0004062091,0.00009458615,0.0001765002,0.0001596885,0.0007388578,0.9251536,0.002391425,0.001279498,0.06336251],"study_design_scores_gemma":[0.00001881335,0.0004776353,0.04090087,0.0002236551,0.0001503054,0.001711844,0.0002504766,0.005187756,0.8619385,0.005342438,0.08360323,0.0001944169],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3367919,0.02790489,0.5850183,0.001467476,0.0007820633,0.0001746512,0.03108229,0.005874441,0.01090395],"genre_scores_gemma":[0.4599186,0.01244487,0.4957405,0.0006108365,0.0002774816,0.0001296064,0.02183926,0.001053494,0.007985367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005294215,"threshold_uncertainty_score":0.01771086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02590516537040181,"score_gpt":0.2344268328444675,"score_spread":0.2085216674740657,"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."}}