{"id":"W2619195952","doi":"10.1007/s10669-017-9645-6","title":"Using expert judgments to inform economic evaluation of ecosystem-based adaptation decisions: watershed management for enhancing water supply for Tegucigalpa, Honduras","year":2017,"lang":"en","type":"article","venue":"Environment Systems & Decisions","topic":"Water resources management and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver Community College; University of British Columbia","funders":"Centre de Coopération Internationale en Recherche Agronomique pour le Développement; World Bank Group","keywords":"Environmental resource management; Watershed; Adaptation (eye); Ecosystem services; Climate change; Watershed management; Business; Productivity; Yield (engineering); Plan (archaeology); Resource (disambiguation); Environmental planning; Ecosystem; Environmental economics; Economics; Environmental science; Computer science; Geography; Ecology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001427794,0.0002923747,0.0003754263,0.0003726026,0.0005411113,0.0002491732,0.0004409276,0.0001019699,0.00006420394],"category_scores_gemma":[0.0000592162,0.0002469426,0.0001593887,0.00003447497,0.00001694516,0.0004700887,0.0001211457,0.00004044018,0.00008538216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007480598,"about_ca_system_score_gemma":0.00001162119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003299837,"about_ca_topic_score_gemma":0.00007027205,"domain_scores_codex":[0.9976553,0.0000443001,0.0009218172,0.0003948291,0.0005772947,0.0004064606],"domain_scores_gemma":[0.9985871,0.0001323081,0.0002295494,0.0008791746,0.00005391802,0.0001179126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005705632,0.00002892308,0.00005972791,0.00007827318,0.0001220728,3.64134e-7,0.0006737225,0.9869765,0.003255035,0.0000555173,0.0004910263,0.008201793],"study_design_scores_gemma":[0.002147588,0.00007534053,0.0003127026,0.0004166367,0.0002015717,4.988848e-7,0.000705397,0.9573139,0.01446129,0.00008861359,0.02395787,0.0003185991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2759696,0.00007784303,0.7178406,0.00006544185,0.0008587031,0.004756805,0.00008708239,0.00004975622,0.0002942931],"genre_scores_gemma":[0.9649776,0.00003938741,0.03258388,0.00001488991,0.0001110136,0.001679229,0.0002847979,0.00007396308,0.0002351917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6890081,"threshold_uncertainty_score":0.9999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08389580911288405,"score_gpt":0.2881629206323723,"score_spread":0.2042671115194882,"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."}}