{"id":"W2014410569","doi":"10.1109/coase.2013.6653961","title":"Managing demand response for manufacturing enterprises via renewable energy integration","year":2013,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Demand response; Renewable energy; Newsvendor model; Leverage (statistics); Electricity; Business; Revenue; Environmental economics; Load management; Contingency; Peak demand; Industrial organization; Computer science; Economics; Finance; Supply chain; 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":[0.0001483822,0.0001612174,0.0001214501,0.0001692763,0.00006393572,0.0000939187,0.0001362993,0.00004435716,0.0002720218],"category_scores_gemma":[0.00001243425,0.0001497572,0.00005558548,0.00005814492,0.000009868285,0.0003067307,0.00004570514,0.00003608247,0.00004072011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009927813,"about_ca_system_score_gemma":0.000002801805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006859851,"about_ca_topic_score_gemma":0.0002376407,"domain_scores_codex":[0.99923,0.00002208486,0.0001970823,0.0001783717,0.00009736324,0.0002751444],"domain_scores_gemma":[0.999559,0.00009784362,0.00001956714,0.0002418654,0.00002137646,0.00006035916],"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":[0.00004986095,0.00001058766,0.00003338018,0.00003955156,0.00006664767,0.000001803717,0.00006343557,0.9194933,0.01780918,0.0001884169,0.04552879,0.01671501],"study_design_scores_gemma":[0.0002784382,0.00003282512,0.000874978,0.00002699755,0.00001521755,0.000002119019,0.00009121728,0.5805774,0.3701563,0.0027166,0.044997,0.000231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03552239,0.00006680923,0.9504541,0.0002114877,0.0004752522,0.0001776842,7.084562e-7,0.0005106004,0.01258096],"genre_scores_gemma":[0.983861,0.00004931192,0.007732707,0.0001816227,0.0001210524,0.0003043844,0.00001362648,0.00004753161,0.007688803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9483386,"threshold_uncertainty_score":0.6106923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006145024480130285,"score_gpt":0.1881328960213115,"score_spread":0.1819878715411812,"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."}}