{"id":"W2071873265","doi":"10.1007/s10640-014-9846-0","title":"Responses to Changes in Domestic Water Tariff Structures: A Latent Class Analysis on Household-Level Data from Granada, Spain","year":2014,"lang":"en","type":"article","venue":"Environmental and Resource Economics","topic":"Water resources management and optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Universidad de Oviedo; Memorial University of Newfoundland; Ministerio de Ciencia e Innovación; Ministerio de Economía y Competitividad","keywords":"Unobservable; Tariff; Latent class model; Econometrics; Consumption (sociology); Economics; Water use; Panel data; Demand curve; Class (philosophy); Estimation; Exploit; Environmental economics; Microeconomics; Computer science; Statistics; Mathematics","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":[0.0001733866,0.0001839729,0.0002217723,0.0002627581,0.00006726377,0.00006747644,0.0002429348,0.00007268854,0.00008862382],"category_scores_gemma":[0.000003851605,0.0001515134,0.00002780719,0.00004958376,0.00003926447,0.00006186921,0.0002237274,0.0001053694,0.00002983214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008259256,"about_ca_system_score_gemma":7.062429e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001470513,"about_ca_topic_score_gemma":0.001026515,"domain_scores_codex":[0.9991069,0.0000462387,0.0001829968,0.0003638702,0.0000701113,0.0002298678],"domain_scores_gemma":[0.9994041,0.00004740418,0.00002268091,0.000433615,3.931215e-7,0.00009184517],"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":[0.00009995985,0.00002651905,0.02442417,0.00001082432,0.0002028253,0.000007378068,0.0008020292,0.9699301,0.0005008103,0.00001433563,0.0002471145,0.003733966],"study_design_scores_gemma":[0.001016975,0.0001063782,0.4187441,0.0000177496,0.0002298826,0.000001613574,0.0002576655,0.4756431,0.0008694308,0.0001966662,0.1022922,0.0006241898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982584,0.00004171152,0.0006651877,0.0002815572,0.00004854237,0.0001496024,0.0003018814,0.0000380435,0.0002150764],"genre_scores_gemma":[0.9977261,0.0001039402,0.000428706,0.0003275199,0.0000798136,0.000008059855,0.0008378671,0.00003250873,0.0004554518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.494287,"threshold_uncertainty_score":0.6178539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01915043656423642,"score_gpt":0.1738202930755504,"score_spread":0.1546698565113139,"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."}}