{"id":"W4405262126","doi":"10.1002/rra.4405","title":"A Kick in the Headwaters: Evaluating a Macroinvertebrate Sampling Method for Ecological Condition Monitoring in Small Streams","year":2024,"lang":"en","type":"article","venue":"River Research and Applications","topic":"Freshwater macroinvertebrate diversity and ecology","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Trent University; Environment Agency; Nottingham Trent University","keywords":"STREAMS; Biomonitoring; Sampling (signal processing); Taxon; Environmental science; Species richness; Biodiversity; Abundance (ecology); Ecology; Hydrology (agriculture); River ecosystem; Biotic index; Biology; Habitat; Geology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001804027,0.00007402731,0.00008863641,0.00009987644,0.000297371,0.0001198442,0.0002233472,0.00006294029,0.0001511442],"category_scores_gemma":[0.00003594284,0.00005619222,0.00002442897,0.0004324992,0.0002391759,0.0001398884,0.0001700523,0.0002728369,0.0001551474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001625401,"about_ca_system_score_gemma":0.00001774624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001290026,"about_ca_topic_score_gemma":0.002778376,"domain_scores_codex":[0.998795,0.0002008394,0.0001368434,0.0003521974,0.0001396079,0.0003755055],"domain_scores_gemma":[0.9991662,0.0006331936,0.0000126061,0.0001208827,0.00001213082,0.00005500313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003405696,0.001769392,0.3642177,0.0006641992,0.00009439078,0.0001444568,0.02644197,0.01102738,0.1249654,0.0562291,0.006352312,0.4077532],"study_design_scores_gemma":[0.0017328,0.001040802,0.1566486,0.0001425158,0.00002915489,0.00004080597,0.007012858,0.4115687,0.005079275,0.3976079,0.01862487,0.0004717299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873497,0.00008326507,0.009440627,0.001437174,0.00002106828,0.00119366,0.00003183894,0.00002284564,0.0004198369],"genre_scores_gemma":[0.9870809,0.00005399389,0.0113957,0.0001071756,0.00005286298,0.001101599,0.00003752216,0.000007081652,0.0001631445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4072814,"threshold_uncertainty_score":0.2291452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1828163688802078,"score_gpt":0.4336371622654027,"score_spread":0.2508207933851949,"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."}}