{"id":"W2587903099","doi":"10.1088/1748-9326/aa60a7","title":"Greenhouse gas mitigation for U.S. plastics production: energy first, feedstocks later","year":2017,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Colcom Foundation; Carnegie Mellon University; National Science Foundation","keywords":"Renewable energy; Greenhouse gas; Tonne; Environmental science; Waste management; Renewable fuels; Renewable resource; Raw material; Engineering; 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","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000837494,0.000272271,0.0001947123,0.0000687776,0.002374884,0.0002157476,0.0007507719,0.0001193385,0.001684882],"category_scores_gemma":[0.0003143952,0.0002625916,0.000135538,0.00006045726,0.002288586,0.0008729592,0.0008079671,0.0003170078,0.0004590544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001458859,"about_ca_system_score_gemma":0.00000855717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003853318,"about_ca_topic_score_gemma":0.0002876101,"domain_scores_codex":[0.9968433,0.0001237085,0.0002860762,0.0007658299,0.001029139,0.0009519003],"domain_scores_gemma":[0.9982752,0.0001436544,0.0001295813,0.001128829,0.00000394797,0.0003187665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004227352,0.0009572395,0.757843,0.00005873643,0.00006807831,0.0000630637,0.0009811096,0.001993267,0.1380441,0.000076289,0.06973615,0.02975618],"study_design_scores_gemma":[0.00114419,0.0004872361,0.7941796,0.00002464404,0.00002523378,0.00002188105,0.0003151648,0.0008722114,0.05310521,0.001900253,0.1473068,0.000617561],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860625,0.00002647273,0.0006795482,0.01109523,0.0002481962,0.0007801996,0.00003695344,0.00003781456,0.001033132],"genre_scores_gemma":[0.9935551,0.0001010066,0.0008614732,0.0004179417,0.0002825389,0.0002813974,0.00004744926,0.00005729625,0.004395786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08493892,"threshold_uncertainty_score":0.9999827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02465776184904039,"score_gpt":0.291766974343011,"score_spread":0.2671092124939706,"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."}}