{"id":"W7010586821","doi":"","title":"Integrating batteries with large-scale wind power: a Canadian case-study","year":2019,"lang":"en","type":"other","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electricity; Revenue; Wind power; Software deployment; Renewable energy; Battery (electricity); Schedule; Investment (military)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005815355,0.0007123815,0.0003771208,0.0009256494,0.001792353,0.001736962,0.001833912,0.001297791,0.007552726],"category_scores_gemma":[0.001737947,0.0002973123,0.0007511968,0.002371144,0.0009226605,0.0008848298,0.0006681039,0.0007625027,0.0005987843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01567834,"about_ca_system_score_gemma":0.009977869,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9322066,"about_ca_topic_score_gemma":0.9540349,"domain_scores_codex":[0.9994475,0.00008175429,0.00001271782,0.00004860653,0.0001728691,0.0002365093],"domain_scores_gemma":[0.9990675,0.0003765144,0.00005471775,0.00005079955,0.0003013098,0.0001492444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001009784,0.001214624,0.05453025,0.0008050277,0.0002170315,0.01014277,0.001271896,0.7799141,0.003744558,0.05948071,0.04095369,0.04671561],"study_design_scores_gemma":[0.0008169255,0.0005870025,0.06416439,0.0003201429,0.0003647925,0.001873713,0.01295411,0.7935917,0.004940719,0.01198603,0.1081111,0.0002893469],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8765437,0.0012156,0.004707338,0.001410296,0.00004789126,0.0004574189,0.005368976,0.0001319572,0.1101169],"genre_scores_gemma":[0.9740559,0.0008515856,0.003891678,0.000091545,0.00001117872,0.00005730746,0.001609144,0.00003860864,0.01939314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06779343,"threshold_uncertainty_score":0.1363853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347019719844234,"score_gpt":0.2558964101710539,"score_spread":0.2424262129726116,"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."}}