{"id":"W2082677532","doi":"10.1016/j.resconrec.2012.07.008","title":"Materials flow analysis of e-waste: Domestic flows and exports of used computers from the United States","year":2012,"lang":"en","type":"article","venue":"Resources Conservation and Recycling","topic":"Recycling and Waste Management Techniques","field":"Environmental Science","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Commission for Environmental Cooperation; Division of Chemical, Bioengineering, Environmental, and Transport Systems; Arizona State University; U.S. Environmental Protection Agency; National Science Foundation","keywords":"Scrap; Reuse; Material flow analysis; Sustainability; Business; Environmental economics; Electronic waste; Exportation; Electronic equipment; Material flow; Waste management; Engineering; Computer science; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005816553,0.0000896702,0.0002264038,0.0001072437,0.00007859424,0.00002982419,0.00008543086,0.00004164449,0.00006403252],"category_scores_gemma":[0.00004344799,0.00006183179,0.00003371873,0.0004274317,0.0001471222,0.00008802307,0.0001078386,0.00003899014,6.112995e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008330054,"about_ca_system_score_gemma":0.000001059543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002421188,"about_ca_topic_score_gemma":0.00006130188,"domain_scores_codex":[0.9991328,0.0001265955,0.0003295545,0.0001344423,0.0001625964,0.0001139902],"domain_scores_gemma":[0.9991174,0.0004417912,0.0002307298,0.0001580858,0.000009213146,0.0000427548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002729379,0.00003689232,0.9640926,0.0000263263,0.0002281098,5.464166e-7,0.004998271,0.009542095,0.01357917,0.00001924193,0.000237892,0.007211572],"study_design_scores_gemma":[0.000344147,0.00004139163,0.8119636,0.0001418958,0.0006215722,0.000001008641,0.002213091,0.1710434,0.009206608,0.0001511421,0.004078054,0.0001941908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966488,0.0001164279,0.002731861,0.0002411136,0.00004997007,0.0001252729,0.00001516683,0.00002565418,0.00004567813],"genre_scores_gemma":[0.9963386,0.000264349,0.003057979,0.0002076664,0.00001987662,0.000003436796,0.00007742282,0.000006073579,0.00002457224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1615013,"threshold_uncertainty_score":0.3660128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792806711283079,"score_gpt":0.2406466506550701,"score_spread":0.2227185835422393,"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."}}