{"id":"W4403441779","doi":"10.1016/j.mfglet.2024.09.099","title":"Optimization and prediction of additively manufactured PLA-PHA biodegradable polymer blend using TOPSIS and GA-ANN","year":2024,"lang":"en","type":"article","venue":"Manufacturing Letters","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"TOPSIS; Materials science; Polymer; Computer science; Biomedical engineering; Composite material; Chemical engineering; Mathematics; Engineering; Operations research","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":[0.0004323402,0.001101753,0.0004146492,0.0006445693,0.0001843901,0.0006987652,0.0003419831,0.0008389669,0.0008732345],"category_scores_gemma":[0.0005555337,0.0004364126,0.0007802887,0.0003409679,0.0001999694,0.0002558612,0.000242337,0.0004457459,0.0001935305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005440346,"about_ca_system_score_gemma":0.0005096333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005047842,"about_ca_topic_score_gemma":0.006553263,"domain_scores_codex":[0.9998633,0.0000248831,0.00001149679,0.00002876054,0.00004791753,0.00002366626],"domain_scores_gemma":[0.9997713,0.0001399673,0.0000371161,0.000008297341,0.00003649821,0.000006872496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005703319,0.00007160281,0.001035196,0.0001009245,0.00003314195,0.00005021482,0.00001965327,0.9582503,0.01795103,0.000231179,0.0001022597,0.02209734],"study_design_scores_gemma":[0.000002758006,0.00005327968,0.0003344488,0.000004092615,0.000009572957,0.000005545393,0.000005615203,0.9936761,0.005723985,0.00007286282,0.0001080041,0.000003662734],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6779102,0.0008657642,0.3121311,0.0001409883,0.00004505389,0.0001489163,0.0002819789,0.00115681,0.007319104],"genre_scores_gemma":[0.9113092,0.0002726433,0.08600621,0.00003322175,0.000003620457,0.0001457361,0.0001503644,0.00003495743,0.002043947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005047842,"threshold_uncertainty_score":0.01003689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334543594357917,"score_gpt":0.197670522286485,"score_spread":0.1843250863429058,"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."}}