{"id":"W2597136739","doi":"10.1016/j.cub.2017.02.031","title":"A Large and Consistent Phylogenomic Dataset Supports Sponges as the Sister Group to All Other Animals","year":2017,"lang":"en","type":"article","venue":"Current Biology","topic":"Marine Invertebrate Physiology and Ecology","field":"Earth and Planetary Sciences","cited_by":571,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Leibniz-Rechenzentrum; Fonds de recherche du Québec – Nature et technologies; Leibniz-Gemeinschaft; Conseil Régional de Bretagne; Bayerische Akademie der Wissenschaften; Institut Universitaire de France; Agence Nationale de la Recherche; Deutsche Forschungsgemeinschaft; National Science Foundation; Compute Canada; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec; National Defense Science and Engineering Graduate; U.S. Department of Defense","keywords":"Biology; Sister group; Bilateria; Lineage (genetic); Evolutionary biology; Phylogenetic tree; Phylogenetics; Clade; Gene; Genetics","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":[0.001613134,0.001115674,0.001354011,0.004123784,0.00184951,0.001760534,0.0004897423,0.0006346681,0.00831647],"category_scores_gemma":[0.0059647,0.0003513998,0.001163007,0.00503355,0.0007246779,0.001065545,0.002544551,0.001291944,0.004265661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003302223,"about_ca_system_score_gemma":0.001641616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002393685,"about_ca_topic_score_gemma":0.007442611,"domain_scores_codex":[0.9987051,0.0001892415,0.0001233902,0.0005786627,0.0002253006,0.0001783428],"domain_scores_gemma":[0.9941679,0.002177973,0.0009143766,0.00106198,0.0007454804,0.0009323897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001432369,0.0002035303,0.5039684,0.003241478,0.003576518,0.001141924,0.001244225,0.001907597,0.3481441,0.002901046,0.0482683,0.08397042],"study_design_scores_gemma":[0.0001062322,0.000173782,0.8450966,0.0004350675,0.002285142,0.002012394,0.001293914,0.002462481,0.01217175,0.004504903,0.1293091,0.0001485518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6077812,0.006560891,0.02146514,0.002476207,0.0004652989,0.00009348021,0.3429324,0.002874458,0.01535085],"genre_scores_gemma":[0.6543201,0.002466759,0.01680294,0.001724049,0.0001152134,0.0001253698,0.3206628,0.001065713,0.002716895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00831647,"threshold_uncertainty_score":0.0278213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04515445788897857,"score_gpt":0.3049239619952134,"score_spread":0.2597695041062348,"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."}}