{"id":"W6932041932","doi":"10.5683/sp3/kbpyix","title":"Crustacean zooplankton and macroinvertebrate traditional taxonomy abundance data and whole community COI eDNA metabarcoding data from 13 high elevation Rocky Mountain lakes [Canada, 2018]","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Parks Canada; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Groupe de recherche interuniversitaire en limnologie","keywords":"Invertebrate; Abundance (ecology); Zooplankton; Relative species abundance; Marsh; Cytochrome c oxidase subunit I; Biodiversity; Taxonomy (biology); Trout","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001150221,0.0004276222,0.0006316537,0.0001085958,0.0009662858,0.0006415322,0.005215474,0.0001501767,0.0001770912],"category_scores_gemma":[0.0001301696,0.0004270429,0.00002483065,0.000186335,0.0001316968,0.001369864,0.003687839,0.000824987,0.000002300708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001722789,"about_ca_system_score_gemma":0.0006425521,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9187743,"about_ca_topic_score_gemma":0.8968968,"domain_scores_codex":[0.9965262,0.0007617225,0.0005357473,0.001070272,0.0007203741,0.0003856908],"domain_scores_gemma":[0.9941969,0.0006395603,0.0004853732,0.004421862,0.00005559456,0.000200747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001619014,0.00004541256,0.00009421486,0.00006676776,0.0001239409,0.00004052673,0.00003290736,0.0000111581,0.000008564302,0.0006662938,0.9978406,0.001053417],"study_design_scores_gemma":[0.0006196711,0.0000466089,0.002164147,0.00005418719,0.000102354,0.00003217765,0.0002049076,0.0164123,0.000001383145,0.001350846,0.9785459,0.0004654969],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002417243,0.001386404,0.002592949,0.0007768726,0.0003664756,0.000473118,0.9939449,0.00005307543,0.0001644646],"genre_scores_gemma":[0.003146376,0.0002619364,0.0006922236,0.000903052,0.0002789808,0.00007782336,0.9945793,0.00001682395,0.00004352523],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02187749,"threshold_uncertainty_score":0.9998181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08279126220797442,"score_gpt":0.235849815682796,"score_spread":0.1530585534748216,"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."}}