{"id":"W2895523422","doi":"10.1007/s10980-018-0707-z","title":"Setting conservation priorities in cities: approaches, targets and planning units adapted to wetland biodiversity and ecosystem services","year":2018,"lang":"en","type":"article","venue":"Landscape Ecology","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université Laval; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ecosystem services; Wetland; Environmental resource management; Unit (ring theory); Stakeholder; Biodiversity; Business; Beneficiary; Prioritization; Scale (ratio); Environmental economics; Spatial planning; Environmental planning; Ecosystem; Environmental science; Geography; Ecology; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002785918,0.0001208841,0.0001968513,0.00007408958,0.0002021557,0.00005214969,0.0001051026,0.000106598,0.000159258],"category_scores_gemma":[0.00001353466,0.0001050078,0.0000069607,0.0001798168,0.00002264217,0.0002148876,0.0002312623,0.00006327225,0.00006485749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003467561,"about_ca_system_score_gemma":0.000008849741,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006774716,"about_ca_topic_score_gemma":0.04984288,"domain_scores_codex":[0.9991233,0.0000738715,0.0001704276,0.0002915209,0.00007819397,0.0002627026],"domain_scores_gemma":[0.9996351,0.0001015684,0.00007044977,0.00009337759,0.00001336114,0.00008613826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003730612,0.000006978767,0.9941527,0.00009487663,0.0000091283,0.000008928288,0.005014648,0.0000667702,0.00003994097,0.000008864667,0.0004490037,0.0001108406],"study_design_scores_gemma":[0.0006324248,0.0001492962,0.9740771,0.00006137213,0.00001231382,0.00002136743,0.005900864,0.01314007,0.00007550596,0.00005254177,0.005690652,0.0001865034],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976512,0.00008967785,0.000005274471,0.0004239482,0.0001398949,0.0002113507,0.00002981677,0.00003271569,0.001416153],"genre_scores_gemma":[0.9992028,0.000013158,0.0001641013,0.0004937503,0.00006708124,0.00001068906,0.00002552157,0.000004866889,0.00001799358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04916541,"threshold_uncertainty_score":0.967495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889393397861953,"score_gpt":0.1985789849205443,"score_spread":0.1796850509419247,"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."}}