{"id":"W2463862144","doi":"10.1007/s11367-016-1163-0","title":"Life cycle inventories of electricity supply through the lens of data quality: exploring challenges and opportunities","year":2016,"lang":"en","type":"article","venue":"The International Journal of Life Cycle Assessment","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal","keywords":"Electricity; Comparability; Data quality; Mains electricity; Consistency (knowledge bases); Computer science; Representativeness heuristic; Quality (philosophy); Environmental economics; Transparency (behavior); Life-cycle assessment; Risk analysis (engineering); Business; Engineering; Operations management; Economics","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":[0.001756631,0.0001147912,0.0002314392,0.00002861432,0.00006827536,0.00002330353,0.001092954,0.00002666388,0.000209354],"category_scores_gemma":[0.0003955095,0.00005753482,0.00006947408,0.00003788741,0.0005418322,0.001343754,0.0007907217,0.0001287446,0.000001395478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002048473,"about_ca_system_score_gemma":0.0001184529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003254746,"about_ca_topic_score_gemma":0.00006181462,"domain_scores_codex":[0.9978824,0.0002417471,0.0006385756,0.0001382843,0.0009432528,0.0001556965],"domain_scores_gemma":[0.9982927,0.0005226897,0.0006514661,0.0003864125,0.00005202264,0.0000946597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001329523,0.0024474,0.4514322,0.0001776429,0.002288414,0.00003853043,0.02505692,0.001859434,0.02576132,0.08107775,0.003017314,0.4055135],"study_design_scores_gemma":[0.001125584,0.0003660031,0.9451559,0.0001133639,0.00007488261,0.00002737666,0.01587087,0.0002412516,0.002594614,0.02505605,0.009178414,0.0001957068],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612328,0.0009075498,0.0003891328,0.03569444,0.0001541796,0.00009771558,0.00003893511,0.000003783298,0.001481422],"genre_scores_gemma":[0.9875588,0.01157976,0.000305603,0.0003912562,0.000106743,0.0000041094,0.000001728741,0.000008012948,0.00004400406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4937237,"threshold_uncertainty_score":0.2346202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2340343428186618,"score_gpt":0.3689471524616894,"score_spread":0.1349128096430275,"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."}}