{"id":"W4311282713","doi":"10.1007/s00267-022-01763-z","title":"Setting Targets for Wetland Restoration to Mitigate Climate Change Effects on Watershed Hydrology","year":2022,"lang":"en","type":"article","venue":"Environmental Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université de Montréal; McGill University; Université Laval","funders":"","keywords":"Wetland; Climate change; Watershed; Environmental science; Hydrology (agriculture); Flood myth; Temperate climate; Drainage basin; Ecology; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004995057,0.0002568174,0.0001955813,0.0001094611,0.0009862994,0.00001702968,0.000287538,0.00003486249,0.0007470288],"category_scores_gemma":[0.000004079015,0.0002571061,0.00007721801,0.00009830386,0.00007655573,0.0001322471,0.001547928,0.0001234209,0.0008933456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004131155,"about_ca_system_score_gemma":3.47654e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001438351,"about_ca_topic_score_gemma":0.00001004741,"domain_scores_codex":[0.9979644,0.0001525575,0.0002273549,0.0006715995,0.000339926,0.000644194],"domain_scores_gemma":[0.9994628,0.0000468977,0.00008577843,0.0003083392,4.512824e-7,0.00009568029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005027816,0.003313294,0.2933161,0.000794665,0.001334439,0.00114035,0.01851983,0.2200453,0.03953217,0.004789982,0.1912333,0.2209528],"study_design_scores_gemma":[0.004298141,0.004006602,0.3030155,0.00002744913,0.0002788548,0.000007938007,0.001008305,0.003997686,0.005061912,0.002455577,0.6745508,0.001291222],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749262,0.00004349453,0.0004840133,0.01173612,0.0007519223,0.004554487,0.00006742335,0.0001315254,0.007304865],"genre_scores_gemma":[0.9866511,0.00003947445,0.0006609381,0.007611755,0.00008312087,0.003756901,0.0001559401,0.00003472811,0.001006023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4833175,"threshold_uncertainty_score":0.9999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00781537433464343,"score_gpt":0.2074331408315657,"score_spread":0.1996177664969222,"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."}}