{"id":"W3160038964","doi":"10.1016/j.jcomc.2021.100156","title":"A group multicriteria decision making with ANOVA to select optimum parameters of drilling flax fibre composites: A case study","year":2021,"lang":"en","type":"article","venue":"Composites Part C Open Access","topic":"Natural Fiber Reinforced Composites","field":"Materials Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Multiple-criteria decision analysis; Drill; Delamination (geology); Drilling; Ultimate tensile strength; Residual; Structural engineering; Computer science; Mathematics; Engineering; Materials science; Mechanical engineering; Composite material; Mathematical optimization; Algorithm; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005986378,0.0005945815,0.001105537,0.0003113588,0.000607022,0.004002802,0.002971676,0.0001139108,0.0003143787],"category_scores_gemma":[0.000128731,0.0005061757,0.0001327679,0.001762848,0.0001164105,0.002424143,0.004369616,0.0002978803,0.0000687619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00014886,"about_ca_system_score_gemma":0.0001430863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000619501,"about_ca_topic_score_gemma":0.0005155165,"domain_scores_codex":[0.9957345,0.0003875203,0.001103162,0.001280274,0.0007234214,0.0007711422],"domain_scores_gemma":[0.9961082,0.001144726,0.0004524934,0.001307107,0.0006700775,0.0003173329],"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":[0.001477271,0.0005518358,0.01689533,0.0001279833,0.000160973,0.004670179,0.00166042,0.020227,0.9520856,0.00003918557,0.000403534,0.001700654],"study_design_scores_gemma":[0.003163343,0.001832846,0.001214863,0.001978622,0.000278761,0.004374782,0.001242966,0.01050059,0.973803,0.00007922683,0.0002487334,0.001282214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867858,0.0002305937,0.009613873,0.00007384493,0.0004580504,0.002402534,0.00006106822,0.0001374839,0.0002367074],"genre_scores_gemma":[0.8181295,0.000002808981,0.1812369,0.0003084569,0.00005350155,0.0001189246,0.00003942265,0.00006723613,0.0000432757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.171623,"threshold_uncertainty_score":0.999739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05489159113227382,"score_gpt":0.3817526865583906,"score_spread":0.3268610954261168,"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."}}