{"id":"W1998117327","doi":"10.1016/j.jenvman.2006.02.002","title":"Toward quantifying the effectiveness of water trading under uncertainty","year":2006,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Impact; Environment and Climate Change Canada; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Alternative trading system; Randomness; Pairs trade; Trading strategy; Algorithmic trading; Emissions trading; Economic shortage; Stochastic programming; Water trading; Water resources; Computer science; Environmental economics; Operations research; Econometrics; Business; Climate change; Economics; Mathematical optimization; Water conservation; Mathematics; Finance; Government (linguistics); Statistics","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":[0.0004942013,0.0001117025,0.0001462155,0.0001035692,0.00004017987,0.00002824437,0.0001744162,0.00002156417,0.00006604019],"category_scores_gemma":[5.187642e-7,0.00006753296,0.00009841884,0.00004948827,0.00003782944,0.0001291939,0.00005644507,0.00008117122,0.000006130253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001214333,"about_ca_system_score_gemma":4.224491e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004411088,"about_ca_topic_score_gemma":6.106458e-7,"domain_scores_codex":[0.9991682,0.00006307929,0.0003086354,0.00006836767,0.0002419939,0.0001497847],"domain_scores_gemma":[0.9997737,0.00003071166,0.00007077061,0.0001033416,0.000003040847,0.00001836939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00004600969,0.00005548076,0.001176269,0.0001980085,0.0001666236,0.00001496557,0.00009327353,0.9860216,0.0108711,0.0005100793,0.0001004751,0.0007461699],"study_design_scores_gemma":[0.008725861,0.0006880634,0.5535711,0.001099548,0.00161069,0.00008855003,0.005682359,0.2126219,0.1798916,0.01151082,0.0229908,0.001518738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665344,0.0002565556,0.02808543,0.000047188,0.000236132,0.0002223316,0.000001426812,0.00001609211,0.004600487],"genre_scores_gemma":[0.9993765,0.0000891436,0.0003458548,0.00001016886,0.00005401036,0.000003333559,0.000005515455,0.00001809132,0.00009741137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7733997,"threshold_uncertainty_score":0.2753914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213407421742151,"score_gpt":0.1861839678858048,"score_spread":0.1740498936683833,"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."}}