{"id":"W2891706207","doi":"10.3390/w10091173","title":"Explaining Water Pricing through a Water Security Lens","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Water resources management and optimization","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; United Nations University Institute for Water, Environment, and Health","funders":"Ministry of Environment; Ministerio del Ambiente, Agua y Transición Ecológica","keywords":"Water pricing; Water security; Water resources; Water scarcity; International trade and water; Water conservation; Integrated water resources management; Food security; Water industry; Environmental economics; Business; Water use; Water supply; Natural resource economics; Water resource management; Environmental science; Economics; Environmental engineering; Agriculture","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001415985,0.0001958624,0.000155022,0.00007672732,0.00015002,0.0001606706,0.0001499518,0.00007571022,0.001158072],"category_scores_gemma":[0.000001127214,0.0001020025,0.00005062872,0.00003675655,0.00004311728,0.0006937474,0.0001410186,0.0001089321,0.001749626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003491374,"about_ca_system_score_gemma":5.158011e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002457923,"about_ca_topic_score_gemma":0.00001197265,"domain_scores_codex":[0.9987941,0.00002017927,0.0002081678,0.0002165914,0.0001445429,0.000616401],"domain_scores_gemma":[0.9996857,0.000003122126,0.0000065655,0.0002389067,0.00003188726,0.00003383619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000134543,0.0001262616,0.002492612,0.0007088031,0.0005837833,0.0001279234,0.5275457,0.08717297,0.3537886,0.0004242484,0.02501342,0.001881111],"study_design_scores_gemma":[0.0002865786,0.00003538604,0.00001880357,0.00001957682,0.00002103052,0.000003042433,0.0001628354,0.01477206,0.7925391,0.0004672214,0.1914185,0.0002559621],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521104,0.0000123143,0.005682987,0.0002751209,0.0005195643,0.0001760463,8.693607e-7,0.000538776,0.04068395],"genre_scores_gemma":[0.9972551,0.000007926887,0.0005794541,0.0002079422,0.000497693,0.00001738897,0.00008526441,0.00005803293,0.001291183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5273829,"threshold_uncertainty_score":0.999755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01222992876282894,"score_gpt":0.1946630738812933,"score_spread":0.1824331451184644,"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."}}