{"id":"W2983063421","doi":"10.1016/j.mineng.2019.106103","title":"Mine operations as a smart grid resource: Leveraging excess process storage capacity to better enable renewable energy sources","year":2019,"lang":"en","type":"article","venue":"Minerals Engineering","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Office of Energy Research and Development; Office of Energy Efficiency; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy","keywords":"Renewable energy; Grid; Flexibility (engineering); Energy storage; Electricity; Smart grid; Peak demand; Computer science; Process engineering; Engineering; Electrical engineering; Power (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002430697,0.0004981862,0.0004539671,0.0004721988,0.0001071222,0.0002198582,0.0004600207,0.0001232321,0.0002529469],"category_scores_gemma":[0.00005250498,0.0005471138,0.00008987129,0.0008074045,0.0000134243,0.0004498812,0.0001407694,0.0002276688,0.0001374854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001977996,"about_ca_system_score_gemma":0.00001829674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001772288,"about_ca_topic_score_gemma":0.0002867161,"domain_scores_codex":[0.9977411,0.00002392423,0.0004506086,0.0005415667,0.0004159063,0.0008268886],"domain_scores_gemma":[0.9989157,0.00005055991,0.00002831884,0.0006560067,0.00008207277,0.0002673249],"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":[0.000004772834,0.00001906866,0.0004109261,0.0001865149,0.00008647346,0.00001547742,0.0006195359,0.9190912,0.06759808,0.0001031057,0.01181749,0.0000473535],"study_design_scores_gemma":[0.0004642995,0.00005134098,0.0002673111,0.0001844494,0.00003231458,0.00001950792,0.000105838,0.5770896,0.06605284,0.00001482136,0.3548549,0.000862765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9598824,0.0002376529,0.02608027,0.0002661737,0.001196497,0.0002950061,0.000012737,0.0009572385,0.01107195],"genre_scores_gemma":[0.9833085,0.00001193015,0.0031986,0.000572423,0.0009727837,0.0002931649,0.00006352244,0.000200667,0.01137843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3430374,"threshold_uncertainty_score":0.999698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008593114841009209,"score_gpt":0.1864644995263717,"score_spread":0.1778713846853625,"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."}}