{"id":"W6892199072","doi":"10.5063/f1zk5f3m","title":"A global database of long-term changes in insect assemblages","year":2020,"lang":"en","type":"dataset","venue":"California Digital Library","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute","funders":"","keywords":"Taxon; Assemblage (archaeology); Species richness; Abundance (ecology); Data set; Set (abstract data type); Data file","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":[0.001761099,0.001404012,0.001677065,0.01006262,0.0004115631,0.002192042,0.001499763,0.0009349862,0.02583202],"category_scores_gemma":[0.007596147,0.0008137574,0.001314184,0.01999798,0.0003065667,0.002169645,0.002052902,0.001475994,0.02439652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171065,"about_ca_system_score_gemma":0.003046748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02442197,"about_ca_topic_score_gemma":0.01801546,"domain_scores_codex":[0.9982519,0.0001624683,0.0005142861,0.0004290327,0.0004957718,0.0001465318],"domain_scores_gemma":[0.9909056,0.00122274,0.001973911,0.001426634,0.003712917,0.0007581796],"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":[0.000275342,0.0000556364,0.02757399,0.004204537,0.0003935188,0.0001112621,0.000236233,0.0009890948,0.001323296,0.003432635,0.9076476,0.05375683],"study_design_scores_gemma":[0.00006420594,0.00003337017,0.06903204,0.0006374363,0.0001216187,0.0001237009,0.0001807886,0.000465274,0.0008738456,0.00123108,0.9271643,0.00007241118],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008039412,0.0002839076,0.0004449487,0.00007924995,0.00005166506,0.00002002173,0.9968007,0.0003376715,0.001177994],"genre_scores_gemma":[0.002008546,0.0003038357,0.001443827,0.00005210906,0.00001921977,0.000109674,0.9953611,0.0001032632,0.0005985111],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02583202,"threshold_uncertainty_score":0.08641672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01733795474625073,"score_gpt":0.2103069497744297,"score_spread":0.1929689950281789,"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."}}