{"id":"W2267845806","doi":"10.3390/en9020072","title":"Visualization of International Energy Policy Research","year":2016,"lang":"en","type":"article","venue":"Energies","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Visualization; Context (archaeology); Management science; Data science; Scale (ratio); Energy policy; Computer science; Engineering; Renewable energy; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002532341,0.00004913083,0.00005319247,0.00006700892,0.00004626561,0.00000765296,0.0001796498,0.00003159088,0.002178309],"category_scores_gemma":[0.0001749582,0.00003255208,0.00002556427,0.0001767344,0.0003943812,0.0002156622,0.0002436736,0.00001860837,0.00005013607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003160601,"about_ca_system_score_gemma":0.00001157108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001351653,"about_ca_topic_score_gemma":0.00005254375,"domain_scores_codex":[0.9991685,0.00007369166,0.0001127299,0.0001270694,0.0003568838,0.000161117],"domain_scores_gemma":[0.9997037,0.00006162872,0.00002873295,0.0001543525,0.000009897125,0.00004169717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004971921,0.0002014161,0.4915545,0.000005061291,0.00001438672,0.000002872723,0.0006683832,0.001075414,0.3395876,0.09765086,0.005769405,0.06342044],"study_design_scores_gemma":[0.0003786265,0.0001172463,0.5085871,0.00001426363,0.000002047041,0.000002208478,0.0003983788,0.0001006682,0.2981716,0.02046825,0.1716132,0.0001463442],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9536321,0.00001664027,0.0005262515,0.000927145,0.00005206514,0.00002577377,0.000003411562,0.00001555671,0.04480105],"genre_scores_gemma":[0.9903902,0.000138836,0.0001043137,0.00004555203,0.00005940942,0.00000666924,0.000002253524,0.000006038003,0.009246727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1658438,"threshold_uncertainty_score":0.9987338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01985171198923296,"score_gpt":0.3432789212808202,"score_spread":0.3234272092915872,"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."}}