{"id":"W4399430613","doi":"10.3390/jmmp8030120","title":"Theoretical Assessment of the Environmental Impact of the Preheating Stage in Thermoplastic Composite Processing: A Step toward Sustainable Manufacturing","year":2024,"lang":"en","type":"article","venue":"Journal of Manufacturing and Materials Processing","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Life-cycle assessment; Process engineering; Environmental impact assessment; Process (computing); Energy consumption; Composite number; Manufacturing process; Manufacturing engineering; Thermoplastic; Production (economics); Parametric statistics; Thermoplastic composites; Environmental science; Materials science; Computer science; Engineering; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065594,0.0002710541,0.0004499846,0.0001887692,0.0001295376,0.0001958128,0.0003933755,0.0001138933,0.00003946047],"category_scores_gemma":[0.00004001278,0.0001483924,0.0001478309,0.00008191126,0.0003023233,0.0002797793,0.0002898501,0.0004951483,1.592018e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002116089,"about_ca_system_score_gemma":0.0001024167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000138093,"about_ca_topic_score_gemma":5.22494e-7,"domain_scores_codex":[0.998325,0.00009818594,0.0007225246,0.0001764905,0.0003308512,0.0003468833],"domain_scores_gemma":[0.9992608,0.0001367491,0.0003605216,0.0001791867,0.00002225087,0.00004048842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003008893,0.0003242011,0.003762993,0.02799686,0.0006946107,0.0001912652,0.009099095,0.06006358,0.5777974,0.001516762,0.00004733984,0.318205],"study_design_scores_gemma":[0.0002562094,0.00006827334,0.09400789,0.001976613,0.00005740268,0.00008518461,0.0007604932,0.002935302,0.8985718,0.001104972,0.0000229783,0.0001528546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974713,0.0005954443,0.001270827,0.00005069261,0.0001986911,0.0001915643,0.00001808043,0.00006475334,0.0001386668],"genre_scores_gemma":[0.9990141,0.00006435592,0.0007858676,0.000003433566,0.00005940008,0.000005368816,6.895002e-7,0.00004118641,0.00002554589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3207744,"threshold_uncertainty_score":0.6051265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006974093795445029,"score_gpt":0.2448730690404723,"score_spread":0.2378989752450273,"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."}}