{"id":"W2535400458","doi":"10.1007/978-3-319-26950-4","title":"Energy Solutions to Combat Global Warming","year":2016,"lang":"en","type":"book","venue":"Lecture notes in energy","topic":"Global Energy and Sustainability Research","field":"Energy","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Beijing; Global warming; Variety (cybernetics); China; Meteorology; Environmental science; Geography; Climatology; Climate change; Computer science; Geology; Oceanography; Archaeology; 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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003556068,0.000950469,0.001058712,0.0005169846,0.0003163254,0.0001055009,0.001256496,0.001707848,0.0008641473],"category_scores_gemma":[0.0008296089,0.000818025,0.000455563,0.0008380568,0.0003287836,0.0001320895,0.000904899,0.0006265971,0.0001348705],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004305291,"about_ca_system_score_gemma":0.001437787,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01526874,"about_ca_topic_score_gemma":0.0728223,"domain_scores_codex":[0.9944224,0.0003924751,0.0008103515,0.001398632,0.0009521234,0.002024037],"domain_scores_gemma":[0.9966958,0.0007411348,0.0001598436,0.001437263,0.0003980324,0.0005679892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001194639,0.00007414003,0.00001839248,0.00003967189,0.00009681813,0.0001764033,0.00004385352,0.05722051,0.00004350688,0.5318767,0.005415726,0.4048748],"study_design_scores_gemma":[0.0003635334,0.0001001563,0.00001325498,0.0001778047,0.00002086125,0.00002415991,0.000003551005,0.000320587,0.0002178471,0.4128855,0.5852767,0.0005960721],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00008382121,0.006910456,0.1362955,0.004504461,0.001769189,0.000237543,0.000221924,0.0004533655,0.8495238],"genre_scores_gemma":[0.3767636,0.001461483,0.001325071,0.00946162,0.004209556,0.0005482715,0.0009357127,0.0004500016,0.6048447],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5798609,"threshold_uncertainty_score":0.9995881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01366279241635864,"score_gpt":0.2671463987472205,"score_spread":0.2534836063308619,"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."}}