{"id":"W2046236338","doi":"10.1007/s00267-012-9854-1","title":"Evaluation of Deposited Sediment and Macroinvertebrate Metrics Used to Quantify Biological Response to Excessive Sedimentation in Agricultural Streams","year":2012,"lang":"en","type":"article","venue":"Environmental Management","topic":"Freshwater macroinvertebrate diversity and ecology","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Rivers Institute, University of New Brunswick","keywords":"Riffle; Sediment; Environmental science; STREAMS; Sedimentation; Hydrology (agriculture); Index of biological integrity; Ecology; Biotic index; Topographic Wetness Index; Invertebrate; Land use; Geology; Habitat; Digital elevation model; Biology; Remote sensing; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001901769,0.000332383,0.00031288,0.0008935775,0.0003107862,0.0006397762,0.0002882737,0.0002643911,0.0002134542],"category_scores_gemma":[0.004193511,0.0001635252,0.0002484681,0.0008780433,0.0002915231,0.0003352769,0.0002729472,0.0001896905,0.00004613562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007134288,"about_ca_system_score_gemma":0.0007608961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023364,"about_ca_topic_score_gemma":0.03257192,"domain_scores_codex":[0.9990931,0.0002833309,0.0001379758,0.0001145955,0.000307976,0.00006305594],"domain_scores_gemma":[0.9974457,0.0006921931,0.0009128901,0.00007043774,0.0006659019,0.0002129254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000797421,0.0002676172,0.974869,0.0000375321,0.00008319573,0.00005902573,0.0001757009,0.001241965,0.009836277,0.00002344814,0.00004013311,0.01256884],"study_design_scores_gemma":[0.00002058271,0.001783042,0.9866005,0.000007134364,0.00006339305,0.00008284698,0.0002140852,0.00597318,0.005096056,0.00002505049,0.0001262047,0.000007998091],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997188,0.00002213593,0.0001582753,0.000002672397,8.210523e-7,0.000007041507,0.0000242842,0.000002513189,0.00006337664],"genre_scores_gemma":[0.9991296,0.0000295156,0.0007003098,0.000004031232,0.000001527322,0.000007362366,0.00004865868,0.000001171898,0.00007793013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01023364,"threshold_uncertainty_score":0.02034813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03274218892207718,"score_gpt":0.2514882783023547,"score_spread":0.2187460893802775,"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."}}