{"id":"W2087308927","doi":"10.1002/ece3.688","title":"Sweeping beauty: is grassland arthropod community composition effectively estimated by sweep netting?","year":2013,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Netting; Arthropod; Species richness; Species evenness; Ecology; Grassland; Abundance (ecology); Sampling (signal processing); Biodiversity; Biology; Environmental science; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001678652,0.00009687107,0.0001406438,0.000007680615,0.001037299,0.00002963659,0.00005717285,0.0001060571,0.0001059009],"category_scores_gemma":[0.00002412517,0.00004413199,0.00002546425,0.00008548875,0.0001151935,0.0001299285,0.00005646026,0.0001673984,0.00008041113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003019431,"about_ca_system_score_gemma":0.000001728403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002385102,"about_ca_topic_score_gemma":0.002010896,"domain_scores_codex":[0.9993323,0.00016534,0.0001047892,0.0001301265,0.00004638545,0.0002210715],"domain_scores_gemma":[0.9996148,0.0002305955,0.00005708596,0.00001341062,0.00003730809,0.00004684722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002350116,0.00009653514,0.6509971,0.000007740955,0.00002692053,6.091664e-7,0.0001165917,0.000001116397,0.3304606,0.00006793472,0.01319834,0.005003032],"study_design_scores_gemma":[0.0001426045,0.0003432588,0.9964316,0.00001481375,0.00001176012,0.000009160431,0.0001344097,0.0005215114,0.000607737,0.001332673,0.0003466613,0.0001037664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975111,0.0003979385,0.00001181021,0.001417805,0.00005170954,0.0001667453,0.00002836096,0.00006654669,0.0003479737],"genre_scores_gemma":[0.9994867,0.00005640525,0.0000276937,0.0002302064,0.00005603043,0.00002010652,0.00008042503,4.959911e-7,0.00004195358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3454345,"threshold_uncertainty_score":0.7978173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02126102978286127,"score_gpt":0.2149830998244882,"score_spread":0.193722070041627,"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."}}