{"id":"W2099726537","doi":"10.1007/s11269-015-0959-1","title":"Developing a Non-Discrete Dynamic Game Model and Corresponding Monthly Collocation Solution Considering Variability in Reservoir Inflow","year":2015,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Universiti Malaya; Imperial College London","keywords":"Inflow; Randomness; Variable (mathematics); Mathematical optimization; Basis (linear algebra); Computer science; Collocation (remote sensing); Hydrogeology; Mathematics; Statistics; Geology","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":[0.0005992348,0.0006904753,0.0009823148,0.0003858487,0.0005555572,0.001118123,0.001078794,0.001987247,0.003537506],"category_scores_gemma":[0.00174551,0.0006307897,0.0009937902,0.0004626106,0.0004926742,0.0008653253,0.001078098,0.00118841,0.0003129447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008152658,"about_ca_system_score_gemma":0.00198623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02838581,"about_ca_topic_score_gemma":0.01804434,"domain_scores_codex":[0.9997995,0.0000608035,0.000007485462,0.0000445937,0.0000443572,0.00004317836],"domain_scores_gemma":[0.9993213,0.0004251546,0.00005307324,0.00002221208,0.000135249,0.00004301586],"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":[0.000008014687,0.00001244236,0.0001284946,0.00001384064,0.000006694057,0.00003569998,0.00001278783,0.9935824,0.0003085947,0.003781636,0.0002072326,0.001902133],"study_design_scores_gemma":[0.000001649246,0.000002304216,0.00001427701,6.662039e-7,0.000001226556,0.000001852072,0.000002872351,0.9994472,0.00002962146,0.0004486252,0.00004866008,0.000001064686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03672063,0.0001102479,0.9545696,0.0002988658,0.00008894698,0.00006759628,0.00008537729,0.0000946341,0.007964098],"genre_scores_gemma":[0.8699787,0.0001660355,0.1181107,0.0001369149,0.00004739844,0.0002452426,0.0001712099,0.0000836473,0.01106014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02838581,"threshold_uncertainty_score":0.05644119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01743480391393302,"score_gpt":0.220392029874234,"score_spread":0.2029572259603009,"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."}}