{"id":"W2736993784","doi":"10.1093/gigascience/gix053","title":"An expanded mammal mitogenome dataset from Southeast Asia","year":2017,"lang":"en","type":"article","venue":"GigaScience","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ontario Museum","funders":"Leibniz-Gemeinschaft; H. Lundbeck A/S; Deutsches Primatenzentrum; Lundbeckfonden; Bundesministerium für Bildung und Forschung","keywords":"Threatened species; Biodiversity; Mammal; Mitochondrial DNA; DNA barcoding; Taxon; Range (aeronautics); Evolutionary biology; Biology; Identification (biology); Barcode; Environmental DNA; DNA sequencing; Ecology; Geography; Habitat; Computer science; DNA; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006954335,0.0005520071,0.0006859335,0.002428401,0.0007457198,0.000632882,0.0009005312,0.0005091266,0.004455477],"category_scores_gemma":[0.00157255,0.0002315257,0.0007054819,0.003077183,0.0002366035,0.0004153809,0.001665766,0.000763241,0.003258835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003683917,"about_ca_system_score_gemma":0.0007942042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009342892,"about_ca_topic_score_gemma":0.02146415,"domain_scores_codex":[0.9995965,0.00005772698,0.00006091029,0.0001593574,0.00006367773,0.00006187422],"domain_scores_gemma":[0.9992949,0.0001144614,0.00011741,0.000171614,0.0001936151,0.0001080354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002942005,0.0006118136,0.3188261,0.007375174,0.001462738,0.005024286,0.006808604,0.01052069,0.2183488,0.002811468,0.2233848,0.2018835],"study_design_scores_gemma":[0.0002367565,0.0002001302,0.5357093,0.0005416235,0.0003772952,0.001891692,0.001300559,0.004876897,0.007977428,0.001311457,0.4454605,0.0001163697],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3148448,0.00193066,0.004454806,0.0002777217,0.00010017,0.0001375032,0.6709118,0.0006777927,0.006664609],"genre_scores_gemma":[0.04033187,0.0004030805,0.005702623,0.0001435034,0.00002454229,0.0002202242,0.9523445,0.00008060203,0.0007490433],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009342892,"threshold_uncertainty_score":0.01857698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02432191127566177,"score_gpt":0.2467431375942283,"score_spread":0.2224212263185666,"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."}}