{"id":"W7024877471","doi":"","title":"Towards including non-gaseous climate agents in impact assessment methods for LCA of wood products in Canada","year":2022,"lang":"en","type":"other","venue":"Espace ÉTS (ETS)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Impact assessment; Environmental impact assessment; Life-cycle assessment; Climate change; Risk assessment; Product (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005632999,0.001116932,0.0006515516,0.001995355,0.001707408,0.004329621,0.00174748,0.0007456846,0.008489525],"category_scores_gemma":[0.006345478,0.0004339216,0.001143355,0.002398768,0.0008507266,0.001468731,0.001494301,0.001072185,0.001623863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03176615,"about_ca_system_score_gemma":0.06622027,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9876259,"about_ca_topic_score_gemma":0.9915489,"domain_scores_codex":[0.9962665,0.0008508578,0.0001038586,0.0002140812,0.002193455,0.0003712961],"domain_scores_gemma":[0.9945964,0.0006880664,0.0001320129,0.0002230644,0.004172537,0.0001879606],"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":[0.0004008308,0.0003993166,0.04447412,0.00147464,0.0004930366,0.0001968215,0.0008937673,0.2718075,0.008852813,0.05226917,0.1008424,0.5178956],"study_design_scores_gemma":[0.0002374208,0.0002674044,0.1114366,0.001642844,0.0006232583,0.0001195865,0.002860862,0.3637017,0.02609828,0.03665768,0.4559432,0.0004111836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.1348028,0.01627072,0.387753,0.01485249,0.000822755,0.002354581,0.03973165,0.004757213,0.3986547],"genre_scores_gemma":[0.4887669,0.008906918,0.3712859,0.001798148,0.0001008608,0.000599491,0.01233875,0.00112795,0.1150751],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03176615,"threshold_uncertainty_score":0.2304807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05050008161809245,"score_gpt":0.4060767939530622,"score_spread":0.3555767123349698,"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."}}