{"id":"W4385863396","doi":"10.1371/journal.pone.0290173","title":"Abundant, diverse, unknown: Extreme species richness and turnover despite drastic undersampling in two closely placed tropical Malaise traps","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Research and Innovation; Deutsche Forschungsgemeinschaft; Lembaga Ilmu Pengetahuan Indonesia; Genome Canada; Ontario Genomics; Bundesministerium für Bildung und Forschung; Ontario Genomics Institute","keywords":"Undersampling; Species richness; Biology; Ecology; DNA barcoding; Biodiversity; Arthropod; Malaise; Sampling (signal processing); Taxon; Evolutionary biology","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":[0.0005286965,0.0003450643,0.0003361278,0.001257126,0.001094929,0.0006819424,0.0005176562,0.0003320882,0.0009921466],"category_scores_gemma":[0.001180832,0.0001796559,0.0001859658,0.000978399,0.0007382745,0.0003743227,0.001033797,0.0002842639,0.000217275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005241088,"about_ca_system_score_gemma":0.0002745748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01754711,"about_ca_topic_score_gemma":0.07160997,"domain_scores_codex":[0.9994763,0.0001065236,0.00003588392,0.0001942137,0.00008665547,0.0001005349],"domain_scores_gemma":[0.999177,0.0001168145,0.0002881593,0.0001118773,0.0001122534,0.0001939297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001312229,0.0000467412,0.9670523,0.00005324316,0.0000995142,0.0003141408,0.00322019,0.0001127902,0.02175091,0.00004755521,0.0001148925,0.007056483],"study_design_scores_gemma":[0.000001299371,0.00001783972,0.9980786,0.000003571806,0.00001149429,0.00009697473,0.0006634353,0.000164852,0.000653761,0.000007961955,0.0002968616,0.000003445819],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992058,0.00006395751,0.000216569,0.0000200455,0.000002218084,0.00001288686,0.0003278385,0.000005124978,0.0001456336],"genre_scores_gemma":[0.9987314,0.0000378986,0.0004980962,0.00002378769,0.000003593864,0.00002352733,0.0004695326,0.000003190738,0.0002089843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01754711,"threshold_uncertainty_score":0.03488994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1292214657139489,"score_gpt":0.2604249046508605,"score_spread":0.1312034389369116,"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."}}