{"id":"W1966474639","doi":"10.2495/si060281","title":"Increased participation in Australian water markets","year":2006,"lang":"en","type":"article","venue":"WIT transactions on ecology and the environment","topic":"Water resources management and optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Australian Research Council","keywords":"Irrigation district; Entitlement (fair division); Production (economics); Irrigation; Business; Water scarcity; Agricultural economics; Agriculture; Scarcity; Value (mathematics); Water supply; Natural resource economics; Water resource management; Economics; Environmental science; Geography; Environmental engineering","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":[0.000110097,0.00006467736,0.00006312671,0.00004844727,0.00006178827,0.000008951366,0.000029284,0.00003930036,0.0003227982],"category_scores_gemma":[2.465978e-7,0.00004275434,0.00001589925,0.00002035273,0.00005719287,0.00003975482,0.000001443599,0.00006843218,0.00004671793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002987284,"about_ca_system_score_gemma":3.17857e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002543539,"about_ca_topic_score_gemma":0.00008821698,"domain_scores_codex":[0.9996201,0.00004435467,0.0001026671,0.00007478513,0.00003612626,0.0001219724],"domain_scores_gemma":[0.999891,0.00001937139,0.000006372504,0.00006987427,5.949213e-7,0.00001280554],"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":[0.0000607031,0.0000938971,0.00203579,0.000007428476,0.00002175436,0.000002232868,0.0001696356,0.9961221,0.00010875,0.0001240669,0.00003156728,0.001222137],"study_design_scores_gemma":[0.005377833,0.00008372791,0.7731022,0.00001106553,0.0001298366,0.000002557179,0.00007494332,0.196753,0.009534388,0.002046809,0.01257333,0.0003103495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829327,0.00001131136,0.01457033,0.0006087743,0.00005530204,0.0002157704,0.000001380721,0.0000349194,0.001569532],"genre_scores_gemma":[0.9987946,0.0000383067,0.00006116914,0.00003271973,0.000008620903,0.00008129569,0.00001062971,0.000006930884,0.0009657421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.799369,"threshold_uncertainty_score":0.3534413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003784088406835651,"score_gpt":0.1607155980221961,"score_spread":0.1569315096153605,"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."}}