{"id":"W2274943355","doi":"10.1016/j.ecoser.2016.01.006","title":"Towards a more structured selection process for attributes and levels in choice experiments: A study in a Belgian protected area","year":2016,"lang":"en","type":"article","venue":"Ecosystem Services","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Selection (genetic algorithm); Variety (cybernetics); Process (computing); Focus group; Qualitative research; Rigour; Computer science; Choice modelling; Protocol (science); Environmental resource management; Management science; Marketing; Business; Engineering; Sociology; Artificial intelligence; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0003941992,0.0001566243,0.0003454228,0.0002099097,0.00005597699,0.0000425346,0.0001253775,0.00008820304,0.00008429721],"category_scores_gemma":[0.00002271571,0.0001421531,0.00002622195,0.0001665314,0.000007700679,0.0004607062,0.00003468595,0.00004700064,0.00001479906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003115671,"about_ca_system_score_gemma":0.00001189857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007368557,"about_ca_topic_score_gemma":0.01124462,"domain_scores_codex":[0.998709,0.00002375275,0.0005516115,0.0004623509,0.00003066058,0.0002226616],"domain_scores_gemma":[0.9995134,0.00003303406,0.0002716342,0.0001264994,0.00001417168,0.00004119918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003114544,0.0001145502,0.9959436,0.0001899936,0.00002680678,3.683955e-7,0.002772041,0.00007859578,0.0002694138,0.00009675664,0.000001553916,0.0004751448],"study_design_scores_gemma":[0.002763856,0.0001841144,0.9881189,0.000121095,0.000003703985,0.00000167505,0.001709043,0.005033454,0.0006102081,0.001156309,0.00009416909,0.0002034512],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969909,0.0002925714,0.0002093711,0.0002044589,0.00008451551,0.001925426,0.0002149421,0.00002356635,0.00005427765],"genre_scores_gemma":[0.9987544,0.000008076622,0.000102722,0.0000323334,0.00003990302,0.0009952167,0.00001207713,0.00001986756,0.00003541579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01050776,"threshold_uncertainty_score":0.627476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09262591907133408,"score_gpt":0.2718790649862816,"score_spread":0.1792531459149475,"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."}}