{"id":"W2072774512","doi":"10.4028/www.scientific.net/amr.97-101.2338","title":"A Method of Multi-Environmental Spatial Scales Division in LCA Based on Chinese Region-Specific","year":2010,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Ministry of Science and Technology of the People's Republic of China","keywords":"Scale (ratio); Division (mathematics); Life-cycle assessment; Environmental quality; Environmental science; Environmental impact assessment; Diversity (politics); Space (punctuation); Environmental resource management; Computer science; Geography; Mathematics; Ecology; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002502019,0.001099417,0.0008958004,0.004293386,0.001470456,0.001778045,0.001357057,0.0004650308,0.004126433],"category_scores_gemma":[0.004050106,0.0005803868,0.001756673,0.005392309,0.001143289,0.002474668,0.001668986,0.001148764,0.0006748302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002286103,"about_ca_system_score_gemma":0.00299641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01702615,"about_ca_topic_score_gemma":0.0164872,"domain_scores_codex":[0.9974167,0.0006367484,0.0002259159,0.0006575665,0.0008968577,0.0001661111],"domain_scores_gemma":[0.9987121,0.0003671971,0.0001108763,0.0001954842,0.0005736167,0.00004080725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001187791,0.0001143955,0.01197087,0.0007929063,0.0003498849,0.00037285,0.001481463,0.1424047,0.01132937,0.240308,0.01153819,0.5792186],"study_design_scores_gemma":[0.0001621241,0.0001901634,0.02331527,0.0001544805,0.0005199421,0.0006642719,0.0009784347,0.7220848,0.01640379,0.1365736,0.09857694,0.00037631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007207069,0.0002387163,0.9854688,0.00008705617,0.00007631942,0.000214407,0.0002322676,0.000484211,0.005991206],"genre_scores_gemma":[0.124369,0.0004552804,0.8689007,0.00009389412,0.00008241662,0.001124942,0.000631225,0.0002760591,0.004066475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01702615,"threshold_uncertainty_score":0.03385413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0255771813547414,"score_gpt":0.3699026634468134,"score_spread":0.344325482092072,"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."}}