{"id":"W2611775912","doi":"10.3897/oneeco.2.e12290","title":"Marine and Coastal Cultural Ecosystem Services: knowledge gaps and research priorities","year":2017,"lang":"en","type":"article","venue":"One Ecosystem","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":164,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"International Council for the Exploration of the Sea","keywords":"Ecosystem services; Marine ecosystem; Sociocultural evolution; Valuation (finance); Millennium Ecosystem Assessment; Geography; Population; Environmental resource management; Ecosystem; Marine habitats; Natural capital; Habitat; Ecology; Business; Political science; Sociology; Environmental science","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001197535,0.0002410797,0.000406334,0.0000583784,0.001476049,0.0008715076,0.0005734457,0.0001435083,0.0003907048],"category_scores_gemma":[0.00001770034,0.0001878892,0.00004285319,0.00008413632,0.00005983136,0.001020491,0.001817344,0.0002046847,0.0009145867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001028252,"about_ca_system_score_gemma":0.00001505656,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004085029,"about_ca_topic_score_gemma":0.2218443,"domain_scores_codex":[0.9978995,0.0001891928,0.0003545896,0.0005856174,0.0004094171,0.0005616659],"domain_scores_gemma":[0.9986973,0.00009401778,0.0001844096,0.0006940211,0.00005212479,0.0002780637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006305148,0.0001036018,0.9859412,0.004071134,0.00008031276,0.00004174998,0.003231231,0.00000483327,0.001070502,0.0004438135,0.0005829753,0.00436564],"study_design_scores_gemma":[0.003315414,0.0005133512,0.7687798,0.002718388,0.0001079937,0.0004383706,0.007648316,0.01727847,0.001897241,0.0007672918,0.1951445,0.001390912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952828,0.0004769234,6.852031e-7,0.0002098695,0.0002701424,0.0004226782,0.00008195163,0.00006153422,0.04564814],"genre_scores_gemma":[0.998076,0.0004439765,0.0000986664,0.00001381098,0.00027118,0.00005061173,0.0000146547,0.00002671531,0.001004442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2177593,"threshold_uncertainty_score":0.9998633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03405727237099952,"score_gpt":0.2901873798849816,"score_spread":0.256130107513982,"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."}}