{"id":"W2337766105","doi":"10.21307/ijssis-2017-561","title":"Industrial And Enterprise Greenhouse Gas Emission Data Analysis System","year":2013,"lang":"en","type":"article","venue":"International Journal on Smart Sensing and Intelligent Systems","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"123 Certification (Canada)","funders":"Program for New Century Excellent Talents in University; Ministry of Education, India","keywords":"Greenhouse gas; Cogeneration; Boiler (water heating); Waste management; Electricity; Thermal power station; Engineering; Exhaust gas; Combustion; Electricity generation; Process engineering; Environmental science; Environmental engineering; Power (physics); Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.000713663,0.0001952849,0.0002635449,0.0001669116,0.0002032184,0.0004934178,0.000326189,0.00009877945,0.0002053096],"category_scores_gemma":[0.0001135461,0.0001496104,0.00007390333,0.0001116561,0.0001351617,0.0004757287,0.0004223391,0.0002576184,0.00009617832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004825176,"about_ca_system_score_gemma":0.000009576575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00485184,"about_ca_topic_score_gemma":0.00006026979,"domain_scores_codex":[0.9979971,0.0002013245,0.0005148877,0.0003925463,0.0006548838,0.0002392426],"domain_scores_gemma":[0.9989401,0.0001098644,0.0002356288,0.000354169,0.00003066762,0.0003296367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001713555,0.0002393356,0.8819877,0.00002198816,0.000839491,0.0002081375,0.001077844,0.002424607,0.001487228,0.00004518581,0.009895495,0.1016016],"study_design_scores_gemma":[0.002268499,0.000715946,0.1393896,0.0008780918,0.0006390955,0.003642459,0.01520941,0.7470958,0.001689957,0.0002753364,0.0868521,0.001343702],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944469,0.00007708134,0.002952677,0.0004673682,0.0009785974,0.0002224749,0.0000166911,0.00002829349,0.0008098825],"genre_scores_gemma":[0.9986904,0.0001567655,0.0001242975,0.00009804558,0.0002751627,0.000001249593,0.00001942927,0.00001375651,0.0006209257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7446712,"threshold_uncertainty_score":0.7334564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03298161954546822,"score_gpt":0.2704535767783428,"score_spread":0.2374719572328746,"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."}}