{"id":"W7071856130","doi":"","title":"Turbine selection for small low-head hydro developments","year":2003,"lang":"en","type":"article","venue":"TSpace","topic":"Cavitation Phenomena in Pumps","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Turbine; Hydraulic turbines; Hydro power; Production (economics); Expert system; Small hydro","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009306537,0.00009922001,0.00008473583,0.00006176202,0.00004307442,0.00001804049,0.00004674539,0.00004472122,0.00009504906],"category_scores_gemma":[0.00005456196,0.000113316,0.00002460688,0.0001679311,0.000006233402,0.00004601941,0.000004381023,0.0000625454,0.00007592454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001362372,"about_ca_system_score_gemma":0.00002035932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006073433,"about_ca_topic_score_gemma":0.00003675785,"domain_scores_codex":[0.9995048,0.00000846524,0.0001101006,0.0001123746,0.00006567317,0.0001985666],"domain_scores_gemma":[0.9997748,0.00003826502,0.00001638076,0.00008043538,0.00003970524,0.00005047632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000136296,0.000407148,0.01007167,0.001683407,0.000713884,0.000007174429,0.02616645,0.3363821,0.4783422,0.03492616,0.07432548,0.03683804],"study_design_scores_gemma":[0.003256529,0.0001442659,0.006248963,0.00008840769,0.00004946567,0.00002773297,0.0005884854,0.03984482,0.3201273,0.003555949,0.6248842,0.001183799],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.916398,0.0001372777,0.05133834,0.00007793466,0.000724326,0.0004129999,0.00000259942,0.0004879235,0.03042056],"genre_scores_gemma":[0.9639165,0.000007330993,0.02954573,0.00006615885,0.00005384723,0.00009699445,0.00001312146,0.00004611988,0.006254132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5505588,"threshold_uncertainty_score":0.4620892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02105947398608742,"score_gpt":0.2716345044246058,"score_spread":0.2505750304385184,"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."}}