{"id":"W3124797348","doi":"10.1371/journal.pone.0171823","title":"Energy and institution size","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Per capita; Energy consumption; Institution; Consumption (sociology); Energy (signal processing); Scale (ratio); Relation (database); Econometrics; Simple (philosophy); Economics; Computer science; Biology; Statistics; Ecology; Physics; Mathematics; Demography; Social science; Sociology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.000581333,0.00004584538,0.0001052224,0.00006317787,0.000549286,0.0003758515,0.000263485,0.00003601238,0.0003781788],"category_scores_gemma":[0.00497846,0.00003457103,0.00001374838,0.00009074661,0.0001121025,0.0003054689,0.0001437951,0.00003764814,0.00007255467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006637401,"about_ca_system_score_gemma":0.00001752984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002249882,"about_ca_topic_score_gemma":0.00002890317,"domain_scores_codex":[0.9989715,0.00001842026,0.0001994871,0.0001690485,0.0005543755,0.00008715582],"domain_scores_gemma":[0.9990154,0.0001708783,0.0001787671,0.0003730929,0.0002202247,0.00004161911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005699832,0.0007992917,0.1132825,0.00001246612,0.00005237792,0.00001610664,0.0005190329,0.000001456656,0.07820767,0.3330235,0.004522777,0.4695058],"study_design_scores_gemma":[0.002668771,0.0002066935,0.5914041,0.0002825408,0.00005514112,0.00001538196,0.000429309,0.04040444,0.08305635,0.2092692,0.071479,0.0007290862],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8963345,0.00001491127,0.0006075476,0.001430823,0.00008521084,0.00002786458,0.000001964476,0.00001952464,0.1014777],"genre_scores_gemma":[0.9888886,0.000009834375,0.001500798,0.0004160615,0.00008943574,0.000002667912,4.419177e-7,0.000002588479,0.009089605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4781216,"threshold_uncertainty_score":0.5960042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2355931470436712,"score_gpt":0.3412425339121675,"score_spread":0.1056493868684963,"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."}}