{"id":"W4378418795","doi":"10.31891/2307-5732-2023-317-1-48-57","title":"BUILDING PREDICTIVE ELECTRICITY CONSUMPTION MODELS FOR TRADITIONAL AND SMART GRID POWER SUPPLY SCHEMES FOR IRON ORE MINES","year":2023,"lang":"en","type":"article","venue":"Herald of Khmelnytskyi National University Technical sciences","topic":"Environmental and Industrial Safety","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Iron Ore Company (Canada)","funders":"","keywords":"Electricity; Predictability; Consumption (sociology); Smart grid; Mains electricity; Grid; Computer science; Volume (thermodynamics); Iron ore; Work (physics); Environmental economics; Engineering; Economics; Electrical engineering; Mathematics; Mechanical engineering; Voltage","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004297351,0.0004069131,0.0004574494,0.0003210724,0.0002149855,0.000748623,0.0007381672,0.0006496636,0.001544983],"category_scores_gemma":[0.001439777,0.0002917253,0.0005747012,0.0003336778,0.0003640963,0.0007791047,0.0004082573,0.0007978152,0.0002852453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008579133,"about_ca_system_score_gemma":0.0006461241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01071173,"about_ca_topic_score_gemma":0.008718445,"domain_scores_codex":[0.9998275,0.00004694586,0.000009293866,0.00004704186,0.00004437904,0.00002486489],"domain_scores_gemma":[0.9995784,0.0002463315,0.00006091407,0.00002879982,0.00007294014,0.0000126446],"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":[0.00001414912,0.00001626258,0.0007143986,0.00001690294,0.000009068987,0.00002094965,0.0000225296,0.9912033,0.0002797432,0.002684354,0.0001487257,0.004869592],"study_design_scores_gemma":[9.475357e-7,0.00000436542,0.0001074303,0.00000141036,0.000002011222,0.000002200659,0.000003494471,0.999059,0.00008978141,0.0006200625,0.0001081408,0.000001211561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1709483,0.0003474643,0.8170063,0.000416547,0.00005543889,0.00009549192,0.0003174146,0.0006339389,0.01017907],"genre_scores_gemma":[0.9764836,0.0001768591,0.01950955,0.00003131514,0.00001376387,0.00009115203,0.0002047763,0.00003147373,0.0034576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01071173,"threshold_uncertainty_score":0.02129877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05967192038208065,"score_gpt":0.2533333884455827,"score_spread":0.193661468063502,"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."}}