{"id":"W2892200825","doi":"10.1111/icad.12323","title":"Improving taxonomic resolution in large‐scale freshwater biodiversity monitoring: an example using wetlands and Odonata","year":2018,"lang":"en","type":"article","venue":"Insect Conservation and Diversity","topic":"Freshwater macroinvertebrate diversity and ecology","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Alberta Museum; Alberta Biodiversity Monitoring Institute; University of Alberta","funders":"","keywords":"Odonata; Dragonfly; Damselfly; Biodiversity; Wetland; Ecology; Lake ecosystem; Biology; Coenagrionidae; Aquatic insect; Habitat","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003641226,0.0003907718,0.0002366829,0.002355041,0.001386885,0.001081827,0.0009124177,0.000335551,0.001002776],"category_scores_gemma":[0.003917206,0.0001743821,0.0001964035,0.003149146,0.0004407165,0.000811577,0.001112459,0.0003277664,0.0001728639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002400747,"about_ca_system_score_gemma":0.002463998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4595729,"about_ca_topic_score_gemma":0.8169734,"domain_scores_codex":[0.9985776,0.0005673751,0.00006524356,0.0001403003,0.0004305907,0.000218882],"domain_scores_gemma":[0.9960246,0.001175085,0.0005252633,0.0003046015,0.001607982,0.0003623588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001484935,0.00008796059,0.7647122,0.0002315796,0.00006588653,0.0004101745,0.002385149,0.001558849,0.01191329,0.0004827942,0.003381546,0.2146221],"study_design_scores_gemma":[0.0000109054,0.00006106532,0.9822899,0.0001236531,0.00003854851,0.0001521578,0.003079052,0.002888563,0.002060121,0.0003974951,0.008871417,0.00002710843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969484,0.001859496,0.01246168,0.0009636071,0.00005299177,0.0002576051,0.001168214,0.0002284238,0.01352387],"genre_scores_gemma":[0.9421484,0.0005116878,0.05451,0.0001665709,0.00002284339,0.00007141245,0.0008490716,0.00001532673,0.001704713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4595729,"threshold_uncertainty_score":0.9137958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06500370264809135,"score_gpt":0.2173274396943717,"score_spread":0.1523237370462804,"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."}}