{"id":"W2417124204","doi":"10.1038/srep20226","title":"With a little help from DNA barcoding: investigating the diversity of Gastropoda from the Portuguese coast","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; Programa Operacional Temático Factores de Competitividade; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ministério da Educação; Genome Canada; Ontario Genomics; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; China Building Materials Academy; Ontario Genomics Institute","keywords":"DNA barcoding; Biology; Gastropoda; Barcode; Evolutionary biology; Mitochondrial DNA; Marine invertebrates; Invertebrate; Zoology; Portuguese; Ecology; Gene; Genetics; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001161102,0.0004118108,0.0004007048,0.004010084,0.001016626,0.0009818999,0.0004995386,0.0006939658,0.0009961871],"category_scores_gemma":[0.004752461,0.00034994,0.0004422986,0.003708599,0.001075512,0.0008819641,0.001019242,0.0004172965,0.0004436946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004507556,"about_ca_system_score_gemma":0.0007797397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01249535,"about_ca_topic_score_gemma":0.02731566,"domain_scores_codex":[0.9990091,0.0001741675,0.000113465,0.0002554116,0.0002903152,0.0001573781],"domain_scores_gemma":[0.9977894,0.0004556266,0.0008063465,0.0002570932,0.0005073577,0.0001842317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002347428,0.00006357527,0.8401836,0.000806348,0.0001374815,0.001787484,0.009246686,0.0002443627,0.08178383,0.0003411553,0.0006018087,0.0645689],"study_design_scores_gemma":[0.000005278004,0.0001313509,0.9812045,0.0002288686,0.00006821377,0.001720209,0.003954753,0.0003157299,0.003247145,0.0001869785,0.008913879,0.00002320865],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910775,0.001494753,0.002344457,0.0002122395,0.00002822082,0.00005688901,0.001187577,0.00001552589,0.003582803],"genre_scores_gemma":[0.9857356,0.001753151,0.009055724,0.0002251631,0.00006326661,0.00006792981,0.002267442,0.00003876361,0.0007930327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01249535,"threshold_uncertainty_score":0.02484524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02809594461989827,"score_gpt":0.2169853412588821,"score_spread":0.1888893966389838,"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."}}