{"id":"W4394014367","doi":"10.1111/fwb.14247","title":"Explaining variation in stream fish productivity with biotic and abiotic variables across wadeable rivers in eastern North America","year":2024,"lang":"en","type":"article","venue":"Freshwater Biology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Ministry of Natural Resources and Forestry; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Abiotic component; Productivity; Ecology; Biotic component; Fish <Actinopterygii>; Fishery; Environmental science; Geography; STREAMS; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004546989,0.0002438231,0.0002512325,0.0007009038,0.0007327484,0.0007411618,0.0004583253,0.0002136841,0.0008409611],"category_scores_gemma":[0.001385239,0.0001922396,0.0003143903,0.001095993,0.0005419817,0.0002352668,0.0005110129,0.0001875968,0.00006560532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003617238,"about_ca_system_score_gemma":0.002421538,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8076382,"about_ca_topic_score_gemma":0.9233068,"domain_scores_codex":[0.9997945,0.0000360867,0.0000160639,0.00006730506,0.0000325408,0.00005345555],"domain_scores_gemma":[0.9991049,0.0002547491,0.0001694566,0.00005219139,0.0002808993,0.000137749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001437511,0.000007708158,0.9968427,0.00000965908,0.00005531305,0.0000345567,0.000295373,0.0006004988,0.0005791981,0.00004452118,0.0001035706,0.001412484],"study_design_scores_gemma":[0.000001426798,0.000003551204,0.9981598,0.000004506957,0.00001049132,0.0000103024,0.0003860848,0.001251415,0.00003225608,0.00003250696,0.0001055876,0.000002059723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995316,0.00003680498,0.000118451,0.00001661354,6.947859e-7,0.000003642807,0.0001664168,0.000003482116,0.0001222464],"genre_scores_gemma":[0.9994854,0.0000328784,0.0001357199,0.000006414148,5.685512e-7,0.000005216605,0.0001981542,0.000002003953,0.0001336568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8076382,"threshold_uncertainty_score":0.3869892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008628105417420309,"score_gpt":0.2157347740671179,"score_spread":0.2071066686496976,"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."}}