{"id":"W324121168","doi":"10.1016/j.ecoser.2015.02.010","title":"Linking marine and terrestrial ecosystem services through governance social networks analysis in Central Patagonia (Argentina)","year":2015,"lang":"en","type":"article","venue":"Ecosystem Services","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Universidad de Los Lagos; Norges Forskningsråd; European Commission; International Development Research Centre","keywords":"Corporate governance; Ecosystem services; Social network analysis; Environmental governance; Social network (sociolinguistics); Environmental resource management; Sociology; Ecosystem; Ecology; Economic geography; Business; Political science; Economics; Social science; Biology; Social capital","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008326863,0.0001406766,0.0001698262,0.00126245,0.0005789538,0.000749093,0.0002706417,0.0001723833,0.001197504],"category_scores_gemma":[0.002191183,0.0001116482,0.0001002137,0.001751991,0.0006784064,0.0004873163,0.0006755766,0.0002137243,0.00005033829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002844028,"about_ca_system_score_gemma":0.001031611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1908141,"about_ca_topic_score_gemma":0.206169,"domain_scores_codex":[0.9994968,0.0002556999,0.00002036316,0.00007796227,0.00005162534,0.00009757987],"domain_scores_gemma":[0.9989986,0.0003914114,0.0002653053,0.0000374086,0.0002131404,0.00009405256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000143553,0.0001266459,0.9270974,0.0001566811,0.00007065721,0.001018087,0.02528505,0.002928145,0.001270943,0.004522967,0.001262714,0.03611724],"study_design_scores_gemma":[0.00001398595,0.00006963586,0.9645108,0.00007068,0.00003226863,0.0001263843,0.0224106,0.005634171,0.0002416913,0.0008872275,0.005993691,0.000008880113],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952591,0.00009862347,0.0003765684,0.0001617432,0.000002418462,0.00003259445,0.0002137406,0.000003848431,0.003851484],"genre_scores_gemma":[0.9987291,0.00009054381,0.0004683071,0.000008978483,0.000002072969,0.00002408257,0.0001731407,0.000001358338,0.0005024259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1908141,"threshold_uncertainty_score":0.3794068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047549557121462,"score_gpt":0.2131195319009758,"score_spread":0.2026440363297611,"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."}}