{"id":"W2923166610","doi":"10.1139/er-2018-0110","title":"A review of input–output model application hot spots in the energy and environment fields based on co-words network analysis","year":2019,"lang":"en","type":"review","venue":"Environmental Reviews","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Shandong Academy of Sciences; National Natural Science Foundation of China","keywords":"Index (typography); Greenhouse gas; Science Citation Index; Social network analysis; Citation; Carbon footprint; Field (mathematics); Ecological footprint; Computer science; Operations research; Regional science; Sociology; Social science; Ecology; Library science; Sustainability; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00343827,0.001315957,0.001101322,0.0116479,0.000472419,0.002444804,0.00124804,0.001124012,0.003307549],"category_scores_gemma":[0.01364452,0.0005299662,0.001800013,0.01402491,0.0006454056,0.003632267,0.0007051791,0.0007836135,0.0006326447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146209,"about_ca_system_score_gemma":0.002688164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005836205,"about_ca_topic_score_gemma":0.005115489,"domain_scores_codex":[0.9979741,0.0006221796,0.0003108963,0.0003540855,0.0006669807,0.00007182352],"domain_scores_gemma":[0.9900114,0.007565208,0.0007600092,0.000233519,0.001346536,0.00008329609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001432159,0.0001094675,0.01228639,0.06104397,0.001325355,0.0007050685,0.001046435,0.02662958,0.001696927,0.05215926,0.02231465,0.8205397],"study_design_scores_gemma":[0.0000536605,0.0003787448,0.03378173,0.04019694,0.004714124,0.002179464,0.002430943,0.119102,0.00554317,0.08043233,0.7108734,0.0003133904],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01162857,0.9298822,0.04023325,0.003470491,0.000712655,0.0001049707,0.000719746,0.0002325919,0.01301552],"genre_scores_gemma":[0.1088405,0.8690428,0.01754544,0.0006296657,0.0006250723,0.0001763083,0.0007167469,0.00006781582,0.002355606],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9883521,"threshold_uncertainty_score":0.01818347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02915834600760663,"score_gpt":0.3010735893385882,"score_spread":0.2719152433309816,"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."}}