{"id":"W3150300839","doi":"10.1051/e3sconf/202124603006","title":"A multi-year analysis of Canadian Arctic historical weather data in support of solar and wind renewable energy deployment","year":2021,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; National Research Council Canada","funders":"","keywords":"Renewable energy; Environmental science; Wind power; Meteorology; Arctic; Software deployment; Solar Resource; Electricity generation; Grid connection; Environmental resource management; Geography; Engineering; Oceanography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006927054,0.0004851873,0.0003681042,0.002116217,0.001122106,0.001053546,0.0004310822,0.0002747249,0.0005813335],"category_scores_gemma":[0.001618832,0.0002089717,0.0005779986,0.003358698,0.0002156276,0.0003043544,0.0003081873,0.0003687258,0.0001005188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004622872,"about_ca_system_score_gemma":0.005449298,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9181261,"about_ca_topic_score_gemma":0.9543595,"domain_scores_codex":[0.9995546,0.00004126506,0.00002285332,0.00007719458,0.0002114042,0.00009266839],"domain_scores_gemma":[0.9989657,0.0001635153,0.00009364297,0.00006029645,0.0006293913,0.0000874937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005150519,0.0001847124,0.5025121,0.0003088089,0.0007244448,0.0009792502,0.0006965972,0.4039595,0.008573538,0.001833018,0.006916622,0.07279641],"study_design_scores_gemma":[0.00001570598,0.00005665264,0.7963381,0.00003716646,0.0001274221,0.00009759737,0.0007684762,0.1906627,0.002895624,0.0001707718,0.00875542,0.00007444974],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982731,0.0002848987,0.002745642,0.0001265974,0.00003416319,0.00003414276,0.009565431,0.0001579173,0.004320057],"genre_scores_gemma":[0.9853013,0.0002588605,0.002978918,0.00001637024,0.000008621247,0.00001604167,0.01058214,0.00003107602,0.0008067696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08187389,"threshold_uncertainty_score":0.1647121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0425085104934939,"score_gpt":0.257575043343499,"score_spread":0.2150665328500051,"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."}}