{"id":"W2404169526","doi":"10.1002/ecs2.1252","title":"A spatially‐explicit assessment of the fish population response to flow management in a heterogeneous landscape","year":2016,"lang":"en","type":"article","venue":"Ecosphere","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Natural Sciences and Engineering Research Council of Canada","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Ministère des Ressources Naturelles et de la Faune; Université de Montréal; Ministry of Natural Resources","keywords":"Electrofishing; Environmental science; Biomass (ecology); Species richness; Hydrology (agriculture); Ecosystem; Ecology; Population; Range (aeronautics); Abundance (ecology); Biology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000257642,0.0000819354,0.0001031133,0.00001513132,0.00006533246,0.000004998439,0.0001940534,0.00002944436,0.003561566],"category_scores_gemma":[0.00002430933,0.00005050667,0.00003457504,0.0001502833,0.00002083521,0.00005656767,0.0004171936,0.000030319,0.0001548318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297142,"about_ca_system_score_gemma":0.000002304924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007776071,"about_ca_topic_score_gemma":0.01675229,"domain_scores_codex":[0.999227,0.00008944785,0.0001653975,0.0002017455,0.0001489162,0.0001674164],"domain_scores_gemma":[0.9996247,0.00004694417,0.00005533276,0.0002464039,0.00000270964,0.00002389817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002147429,0.0001356797,0.8866751,0.00001301733,0.00004473911,0.00001584943,0.0001695029,0.0111107,0.0002091484,0.00009169711,0.0901996,0.01112028],"study_design_scores_gemma":[0.0003849774,0.00007474452,0.9929155,0.00002242327,0.000008483837,5.01632e-7,0.00002886476,0.0004506321,0.00006146305,0.0002268031,0.005751721,0.00007385049],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668659,0.00000182885,0.0003085509,0.004052087,0.000109774,0.0004291591,0.000005494916,0.0000138307,0.02821337],"genre_scores_gemma":[0.9945651,0.00001184891,0.00125074,0.0008051457,0.000007208346,0.00008073856,7.917636e-7,0.000006417552,0.003272056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1062405,"threshold_uncertainty_score":0.9973493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005623323541464984,"score_gpt":0.2252105773734943,"score_spread":0.2195872538320293,"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."}}