{"id":"W7020676267","doi":"","title":"Life Cycle Assessment Based Greenhouse Gas Emission Reductions, Cross Country Analysis and Algorithm Aided Prediction for Lightweighted Composite Auto Parts","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Automotive industry; Normalization (sociology); Greenhouse gas; Life-cycle assessment; Database normalization; Data set; Set (abstract data type); Hotspot (geology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001239491,0.0007369909,0.0005607971,0.001733094,0.0002930402,0.001007682,0.0004571227,0.0006226561,0.00228344],"category_scores_gemma":[0.002151585,0.0002877037,0.001345172,0.001436748,0.0002572259,0.000779411,0.0005026917,0.0007473881,0.0005195992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009331568,"about_ca_system_score_gemma":0.000746763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01806559,"about_ca_topic_score_gemma":0.01226716,"domain_scores_codex":[0.9997028,0.00008002954,0.00001853123,0.00009113827,0.00007440623,0.00003327313],"domain_scores_gemma":[0.9991215,0.0004888081,0.0001098807,0.00008354023,0.0001757343,0.00002056305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007668622,0.00006288064,0.008808605,0.0000862254,0.00007307756,0.00003974325,0.00002305619,0.9459251,0.001147648,0.001523428,0.0009385059,0.04129515],"study_design_scores_gemma":[0.000003012613,0.00003014669,0.003276021,0.000008313847,0.00001462649,0.000009196036,0.00002179865,0.993067,0.00160495,0.001172385,0.0007831252,0.00000928127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5619151,0.001445449,0.4207689,0.0004988568,0.0001285586,0.0001929027,0.004079927,0.001767252,0.00920311],"genre_scores_gemma":[0.9165534,0.0004112304,0.07613302,0.00004031313,0.00001685098,0.0001340565,0.003104727,0.0001185685,0.003487856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01806559,"threshold_uncertainty_score":0.03592092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007956185983389293,"score_gpt":0.3216297176145245,"score_spread":0.3136735316311352,"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."}}