{"id":"W3122204442","doi":"","title":"Emptying the Tank: Getting the Most Out of Limited Data","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Variation (astronomy); Value (mathematics); Marginal value; Simple (philosophy); Marginal cost; Data science; Computer science; Econometrics; Economics; Statistics; Mathematics; Microeconomics; Epistemology; Philosophy","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":[0.001978558,0.0001272434,0.0001202063,0.00004143559,0.000375119,0.00006000287,0.0009662143,0.00004722898,0.00002006677],"category_scores_gemma":[0.0001449711,0.00007247247,0.00004683893,0.0002042374,0.00009085005,0.0001761539,0.0001255245,0.001328499,0.00001228653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001154045,"about_ca_system_score_gemma":0.0002585175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002918322,"about_ca_topic_score_gemma":0.00113246,"domain_scores_codex":[0.9981291,0.00005590771,0.0002932003,0.0001130863,0.0001974012,0.001211299],"domain_scores_gemma":[0.9991532,0.0001482868,0.0001064318,0.0004963619,0.00006321256,0.00003252397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001039253,0.000087622,0.007745857,0.00009454219,0.00275458,0.00001418591,0.01206179,0.02455603,0.03343825,0.1101723,0.01084371,0.7981272],"study_design_scores_gemma":[0.002503286,0.0009532712,0.001670746,0.0007851077,0.0005346962,0.002447436,0.01710702,0.5050831,0.01478859,0.07673526,0.3756541,0.001737323],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.897096,0.01805462,0.05001078,0.002208468,0.003424577,0.0002398674,0.00002599266,0.000281905,0.02865777],"genre_scores_gemma":[0.9972056,0.0009865541,0.0001228669,0.00008365092,0.001238748,0.000001067754,0.000005600685,0.00003302863,0.0003228999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7963899,"threshold_uncertainty_score":0.5771739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02009293178849087,"score_gpt":0.2396957507424095,"score_spread":0.2196028189539186,"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."}}