{"id":"W2315879361","doi":"10.1061/40927(243)222","title":"Multi-Objective Modeling to Improve Wetland Diversity around Lake Ontario","year":2007,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2007","topic":"Environmental Conservation and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wetland; Hydropower; Shore; Environmental science; Flooding (psychology); Environmental resource management; Hydroelectricity; Adaptive management; Biota; Water resource management; Hydrology (agriculture); Ecology; Oceanography; Engineering; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003996418,0.000319563,0.0002159109,0.0001079268,0.0005868891,0.00006864685,0.0002643382,0.0000675031,0.005289115],"category_scores_gemma":[0.000001956899,0.00026301,0.00007118687,0.0000671419,0.0002755355,0.000281885,0.002169768,0.0002163815,0.001197811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004863295,"about_ca_system_score_gemma":0.000001092678,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0106354,"about_ca_topic_score_gemma":0.2826313,"domain_scores_codex":[0.9979305,0.00003341596,0.0003063435,0.0006754902,0.0004392184,0.000615082],"domain_scores_gemma":[0.9992676,0.00002016911,0.00005557335,0.0002853458,0.000001371063,0.0003699432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002090071,0.0002299255,0.9712988,0.000007958795,0.00005066967,0.00005979366,0.006912476,0.002684543,0.01439252,0.000002770203,0.0007070292,0.003444537],"study_design_scores_gemma":[0.00140473,0.000125145,0.6692476,0.00001640344,0.00005344295,0.000008382811,0.0009446081,0.001350426,0.003883594,0.00005769324,0.3222537,0.0006542385],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913108,0.00002264626,0.0011938,0.0002230561,0.0002711137,0.0005299589,0.00001836785,0.00004567753,0.006384588],"genre_scores_gemma":[0.9498261,0.00001330476,0.001149838,0.002447871,0.00004832328,0.00001579643,0.00003110016,0.00002546788,0.04644221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3215467,"threshold_uncertainty_score":0.9999822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124192648131048,"score_gpt":0.1964960767785592,"score_spread":0.1840768119654544,"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."}}