{"id":"W3040997842","doi":"10.1016/j.cattod.2020.07.001","title":"Preface to the special issue of “the 10th International Conference on Environmental Catalysis &amp; the 3rd International Symposium on Catalytic Science and Technology in Sustainable Energy and Environment-Energy Session (ICEC&amp;EECAT2018_Energy), September 22–26th 2018, Tianjin, China”","year":2020,"lang":"en","type":"article","venue":"Catalysis Today","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Session (web analytics); Sustainable energy; Catalysis; Energy (signal processing); Engineering; Nanotechnology; Chemistry; Physics; Computer science; Renewable energy; Materials science; Electrical engineering; World Wide Web; Organic chemistry","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"],"consensus_categories":[],"category_scores_codex":[0.0006436017,0.0003413947,0.0004444511,0.0003977499,0.0003200883,0.0001565339,0.001521244,0.0001457494,0.0008386535],"category_scores_gemma":[0.0001571532,0.0002582041,0.0001010499,0.0005723457,0.0008786469,0.000331887,0.001277446,0.000241893,0.0001860318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00069546,"about_ca_system_score_gemma":0.0000419495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004908688,"about_ca_topic_score_gemma":0.0002277471,"domain_scores_codex":[0.9974483,0.00004740588,0.000703187,0.001085985,0.0002821716,0.0004329622],"domain_scores_gemma":[0.9982283,0.00006681299,0.0005412833,0.0009864385,0.00001969936,0.0001575391],"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":[0.0006292768,0.002027659,0.1140951,0.00009185401,0.00152381,0.00002777337,0.009601963,0.07202788,0.03433484,0.6902462,0.05758502,0.01780859],"study_design_scores_gemma":[0.0008995368,0.0001212993,0.01700443,0.00003129459,0.00005879627,0.00001239806,0.0008258728,0.004199743,0.01264794,0.002234739,0.9613872,0.0005768196],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8812267,0.000563523,0.0006974478,0.07808828,0.0008776073,0.0004988488,0.0003823086,0.00003447612,0.03763086],"genre_scores_gemma":[0.9869532,0.0005755855,0.00005829424,0.001076212,0.0003629363,0.00008275835,0.0001257273,0.00003486608,0.01073043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9038021,"threshold_uncertainty_score":0.999987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0121870008616844,"score_gpt":0.2014210107527937,"score_spread":0.1892340098911093,"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."}}