{"id":"W2531106476","doi":"10.1371/journal.pone.0164372","title":"Characterization of Macroinvertebrate Communities in the Hyporheic Zone of River Ecosystems Reflects the Pump-Sampling Technique Used","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Freshwater macroinvertebrate diversity and ecology","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Environment Research Council; Trent University; Nottingham Trent University","keywords":"Species richness; River ecosystem; Sampling (signal processing); Hyporheic zone; Replicate; Ecology; Abundance (ecology); Rarefaction (ecology); Taxon; Biodiversity; STREAMS; Community structure; Ecosystem; Temperate climate; Freshwater ecosystem; Biology; Environmental science; Sediment; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004139882,0.0002209059,0.0002840694,0.0007181762,0.0004028841,0.0006683691,0.0001502904,0.0002826737,0.0004887056],"category_scores_gemma":[0.0008407664,0.0001684409,0.0002272714,0.0003949931,0.0003384058,0.0004332402,0.0003176311,0.0001595294,0.0001670774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014873,"about_ca_system_score_gemma":0.0001675989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004285619,"about_ca_topic_score_gemma":0.0154296,"domain_scores_codex":[0.9995309,0.0001228384,0.00004359719,0.0001458991,0.0001099736,0.00004675191],"domain_scores_gemma":[0.9994374,0.000103618,0.0002516491,0.00003763886,0.000113749,0.00005595939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001282303,0.00004947383,0.8731149,0.00006451953,0.0000805411,0.00005927333,0.00093907,0.0001235104,0.1146598,0.00004309859,0.00003411688,0.01070345],"study_design_scores_gemma":[7.539963e-7,0.00007752099,0.9987088,0.000002713371,0.000006383691,0.00005115032,0.0001170384,0.00008634913,0.0008631472,0.000005934992,0.00007846971,0.000001731358],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993823,0.00008604365,0.0003470682,0.000002638095,5.913157e-7,0.000004949921,0.0000402745,0.000001942572,0.0001342984],"genre_scores_gemma":[0.9987535,0.00008196176,0.0008019038,0.000009423196,0.000002278633,0.00001485307,0.0001793839,0.000001992205,0.0001546941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004285619,"threshold_uncertainty_score":0.008521378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05605955760569819,"score_gpt":0.2101586057552782,"score_spread":0.15409904814958,"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."}}