{"id":"W4385431408","doi":"10.1016/j.softx.2023.101485","title":"EcoDynElec: Open Python package to create historical profiles of environmental impacts from regional electricity mixes","year":2023,"lang":"en","type":"article","venue":"SoftwareX","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Bundesamt für Energie","keywords":"Python (programming language); Electricity; Computer science; Open source; Electricity system; Electricity generation; Database; Programming language; Engineering; Software; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004003764,0.0002796619,0.0003743613,0.00007628802,0.0001872659,0.00004380629,0.0007430228,0.0001303166,0.003510438],"category_scores_gemma":[0.0002081206,0.0002583936,0.0001352804,0.00048977,0.0002001448,0.0004147862,0.001089154,0.0001840629,0.001273051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002255054,"about_ca_system_score_gemma":0.00002914633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005956261,"about_ca_topic_score_gemma":0.0001851418,"domain_scores_codex":[0.9976258,0.0001339154,0.0003947293,0.0006276567,0.0005797102,0.0006381381],"domain_scores_gemma":[0.9987038,0.0001832011,0.0001365354,0.0005665076,0.000003383749,0.0004065276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000213007,0.0004635597,0.849523,0.00001532609,0.00003215103,0.00003583691,0.001271095,0.00007764992,0.1069486,0.00001050245,0.03243553,0.008973778],"study_design_scores_gemma":[0.0004069341,0.0003279777,0.9655297,0.00001163394,0.00002082473,0.000003258972,0.0002149982,0.00004881865,0.01970581,0.001960457,0.01143978,0.0003298393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972694,0.00005528941,0.0001639261,0.0005448692,0.0000837623,0.0007305521,0.0001932764,0.0001220998,0.0008368324],"genre_scores_gemma":[0.9945489,0.00006610965,0.001654602,0.0003263887,0.00003615718,0.00006061522,0.0001272229,0.00004256944,0.003137472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1160067,"threshold_uncertainty_score":0.9999868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01814548460161823,"score_gpt":0.2564998175530732,"score_spread":0.238354332951455,"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."}}