{"id":"W2034123500","doi":"10.1672/07-170.1","title":"Identifying wetland compensation principles and mechanisms for Atlantic Canada using a Delphi approach","year":2008,"lang":"en","type":"article","venue":"Wetlands","topic":"Environmental Conservation and Management","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Wisconsin Department of Natural Resources","keywords":"Compensation (psychology); Transparency (behavior); Wetland; Environmental resource management; Business; Government (linguistics); Process (computing); Environmental planning; Political science; Computer science; Environmental science; Ecology; Law; Psychology","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":[0.01100757,0.0002906268,0.0002797273,0.001729957,0.005615518,0.002689828,0.001102985,0.0007557833,0.002441015],"category_scores_gemma":[0.01327471,0.0003203588,0.0004075257,0.001255912,0.00138988,0.001167162,0.001606309,0.0007733521,0.0001102584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01909132,"about_ca_system_score_gemma":0.04402036,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6663608,"about_ca_topic_score_gemma":0.8638468,"domain_scores_codex":[0.9970046,0.001041245,0.0001351213,0.0001850946,0.0006275725,0.001006457],"domain_scores_gemma":[0.9936488,0.002802216,0.0003898094,0.0002367093,0.002432525,0.0004898867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005566312,0.0005798779,0.3425446,0.0006252592,0.0002377593,0.0008178774,0.06619229,0.04200128,0.008667531,0.07384134,0.01101896,0.4529165],"study_design_scores_gemma":[0.0001513231,0.0007644744,0.3851756,0.0007998545,0.0003672444,0.0004973799,0.352472,0.1381729,0.01115299,0.04827151,0.06191035,0.0002643697],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9180404,0.0002646094,0.05006029,0.003509473,0.00002372381,0.001758176,0.0002044027,0.00006366622,0.02607526],"genre_scores_gemma":[0.9542921,0.0001344543,0.04199131,0.000184998,0.000002715573,0.0002496405,0.00006648537,0.000007881066,0.003070422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3336392,"threshold_uncertainty_score":0.6712081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06257688332512067,"score_gpt":0.2303309403165357,"score_spread":0.167754056991415,"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."}}