{"id":"W4412423730","doi":"10.1002/app.57619","title":"Predicting Abrasion Resistance in Thermoplastic Polyurethanes Using Machine Learning","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Polymer Science","topic":"Epoxy Resin Curing Processes","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thermoplastic; Abrasion (mechanical); Materials science; Composite material; Polymer science; Thermoplastic polyurethane; Chemical resistance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008774256,0.0006002485,0.0003852678,0.000795648,0.0001140058,0.0004098332,0.0003624221,0.0004988373,0.0004507382],"category_scores_gemma":[0.001858859,0.0002071263,0.0004211875,0.0003849337,0.000154717,0.0004583284,0.0001802908,0.0005136215,0.0002161305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002954226,"about_ca_system_score_gemma":0.0003204849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002994467,"about_ca_topic_score_gemma":0.002735616,"domain_scores_codex":[0.9997738,0.00006425144,0.00001619258,0.00005472303,0.00006995078,0.00002111532],"domain_scores_gemma":[0.9990649,0.000542853,0.0001592283,0.00005478465,0.0001571452,0.00002115874],"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":[0.0001867312,0.0003797152,0.01876041,0.00008718287,0.00005070997,0.0000574255,0.00002810965,0.8624347,0.04022881,0.0002227448,0.0003009877,0.07726248],"study_design_scores_gemma":[9.918142e-7,0.00002755304,0.001347262,0.000001734759,0.000002716,0.000004420636,0.000002433882,0.9943916,0.004149646,0.00004110421,0.00002832502,0.000002354185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8416115,0.0003402771,0.1562913,0.0000671614,0.00001577894,0.00004022456,0.000168609,0.0008345639,0.0006306994],"genre_scores_gemma":[0.9843701,0.00006509224,0.01513291,0.00001019213,0.000003820156,0.0000181109,0.0001300753,0.00001152285,0.0002581752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002994467,"threshold_uncertainty_score":0.005954087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007439057699208246,"score_gpt":0.238277425857566,"score_spread":0.2308383681583578,"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."}}