{"id":"W7056126635","doi":"","title":"Developing Parsimonious and Efficient Algorithms for Water Resources Optimization Problems","year":2012,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Particle accelerators and beam dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University at Buffalo; University of Waterloo","keywords":"Stability (learning theory); Filter (signal processing); Term (time); Set (abstract data type); Genetic algorithm; Heuristic","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001893372,0.001644963,0.001229328,0.001065302,0.0004949645,0.001245188,0.001541889,0.001644972,0.002351382],"category_scores_gemma":[0.005902238,0.0009260522,0.0009588599,0.001353533,0.0009589576,0.001386268,0.002043295,0.002318922,0.0007790105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000862178,"about_ca_system_score_gemma":0.001522893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002128329,"about_ca_topic_score_gemma":0.002436475,"domain_scores_codex":[0.9990388,0.0003444303,0.00006282407,0.0001607505,0.0003304954,0.00006270329],"domain_scores_gemma":[0.9978823,0.00166562,0.0001273558,0.00009481697,0.0001859341,0.00004389804],"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":[0.00002927483,0.00004141291,0.0003674901,0.0001182675,0.00002778866,0.00004036399,0.00007459103,0.9076825,0.001087312,0.01905818,0.0009121874,0.07056074],"study_design_scores_gemma":[0.0000149072,0.00001644902,0.00003377492,0.000009565737,0.00000384482,0.00001130786,0.000009522403,0.9915546,0.0002574969,0.007276692,0.0008089221,0.000002948814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004674209,0.0003111727,0.9928486,0.0001290696,0.00001912161,0.00007817118,0.00002239454,0.0001546199,0.001762648],"genre_scores_gemma":[0.06943052,0.0004921369,0.9280003,0.0001129486,0.00003678829,0.0003067971,0.0001064438,0.00007923917,0.001434809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002351382,"threshold_uncertainty_score":0.01001322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285037710265871,"score_gpt":0.1962164576023475,"score_spread":0.1833660804996888,"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."}}