{"id":"W4399702178","doi":"10.1016/j.ecolecon.2024.108259","title":"Texas water markets: Understanding their trends, drivers, and future potential","year":2024,"lang":"en","type":"article","venue":"Ecological Economics","topic":"Water resources management and optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Commodity; Water trading; Water scarcity; Scarcity; Population; Water use; Water resources; Business; Agriculture; Natural resource economics; Agricultural economics; Economics; Geography; Water conservation; Ecology; Finance; Market economy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008716348,0.0001835898,0.0001971141,0.001018133,0.0003915891,0.002799147,0.0005208687,0.0005832818,0.004858146],"category_scores_gemma":[0.00253747,0.0001367275,0.0001843103,0.002446425,0.0007599472,0.004931535,0.0005579178,0.001024522,0.0002134046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003206664,"about_ca_system_score_gemma":0.002180174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03583553,"about_ca_topic_score_gemma":0.08560981,"domain_scores_codex":[0.9997968,0.00004042021,0.000008076798,0.00003830217,0.00004620524,0.00007016792],"domain_scores_gemma":[0.9982686,0.0005424583,0.000623574,0.00003660012,0.0003582578,0.0001705002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003430517,0.0004234209,0.5356257,0.0002759966,0.0001245695,0.0004219145,0.002240928,0.02233895,0.001608162,0.2245293,0.04072646,0.1713414],"study_design_scores_gemma":[0.00005014066,0.0002188632,0.6512764,0.0003066293,0.0001017887,0.0002610031,0.02055069,0.1142079,0.001160295,0.1319943,0.07978364,0.00008834394],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9093989,0.005415626,0.005964906,0.04753131,0.00009191027,0.00003380436,0.00176375,0.00005536947,0.02974443],"genre_scores_gemma":[0.9930636,0.002624129,0.0007068019,0.0004014392,0.00005887118,0.00001235574,0.0002275101,0.000007953691,0.002897457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03583553,"threshold_uncertainty_score":0.0712539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009520393304712958,"score_gpt":0.1601851558846362,"score_spread":0.1506647625799233,"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."}}