{"id":"W2900462580","doi":"10.1139/gen-2018-0107","title":"Recalibrating the molecular clock for Arctic marine invertebrates based on DNA barcodes","year":2018,"lang":"en","type":"article","venue":"Genome","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Molecular clock; Biology; Marine invertebrates; Invertebrate; Arctic; Ecology; Range (aeronautics); Sister group; Genetic divergence; Divergence (linguistics); Cytochrome c oxidase subunit I; Taxon; Phylogenetics; Clade; Genetic diversity; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005551929,0.00008724163,0.00009790769,0.00004511811,0.0003518269,0.00003154757,0.0002589975,0.00006971295,0.007358585],"category_scores_gemma":[0.0001827489,0.00005390418,0.00004109339,0.0001155673,0.0002185187,0.00003688707,0.00002764868,0.0001151075,0.0004153968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003000496,"about_ca_system_score_gemma":0.00004834338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002429193,"about_ca_topic_score_gemma":0.001239882,"domain_scores_codex":[0.9991445,0.0001348019,0.0001104523,0.0002078394,0.00007796253,0.0003244417],"domain_scores_gemma":[0.9992893,0.0003767028,0.00003058162,0.0001990879,0.00003997945,0.00006429721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00025395,0.00002047963,0.9844235,0.00003313532,0.0000363354,0.000009111606,0.00006427689,0.0008036376,0.0009467254,0.00111435,0.0003376343,0.01195691],"study_design_scores_gemma":[0.0003169935,0.001063113,0.8719885,0.000004243356,0.000009480546,0.000005444771,0.00003224243,0.1042993,0.0007956406,0.005607394,0.0157369,0.0001406859],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783959,0.0001018143,0.0003227505,0.003655096,0.0002390375,0.0004295227,0.0000252345,0.00003756052,0.01679308],"genre_scores_gemma":[0.9953634,0.000004382075,0.0006487611,0.003008114,0.000207974,0.000008920333,0.0001345216,0.000003244532,0.0006206809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1124349,"threshold_uncertainty_score":0.9935488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861824594107293,"score_gpt":0.2348160171040982,"score_spread":0.2161977711630253,"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."}}