{"id":"W3122338266","doi":"10.20944/preprints201904.0087.v1","title":"Preparation of Thermoplastic Polyurethane (TPU) Perforated Membrane via CO&lt;sub&gt;2&lt;/sub&gt; foaming and Its Particle Separation Performance","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Polymer Foaming and Composites","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Thermoplastic polyurethane; Materials science; Membrane; Composite material; Tear resistance; Polyurethane; Ultimate tensile strength; Blowing agent; Elastomer; Thermoplastic elastomer; Particle (ecology); Chemical engineering; Polymer; Chemistry","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001195216,0.0005441498,0.00075253,0.0001622882,0.0002368464,0.00009679472,0.0005819182,0.0004126203,0.0003301569],"category_scores_gemma":[0.0001606042,0.0005467236,0.0001248411,0.0002222318,0.0001630945,0.0004786651,0.0007883864,0.0004286335,0.00113597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001135529,"about_ca_system_score_gemma":0.0002350154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003399433,"about_ca_topic_score_gemma":0.00001423001,"domain_scores_codex":[0.9962307,0.0003601018,0.0009394438,0.001215674,0.0006674822,0.0005865745],"domain_scores_gemma":[0.9974115,0.0002057582,0.0008338976,0.001074855,0.0002917324,0.0001822636],"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":[0.0004345733,0.0001461026,0.02443158,0.0006492027,0.00006401733,0.000002522028,0.001643219,0.004914788,0.9671355,0.00005860302,0.000006363267,0.0005135055],"study_design_scores_gemma":[0.0005184854,0.0001295453,0.0549813,0.0003624815,0.0001210266,0.00002322437,0.00001698203,0.09986642,0.8434336,0.0000513695,0.00002831523,0.0004672026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951293,0.001379449,0.0004395316,0.00005490036,0.0007617224,0.0009193501,0.00005271751,0.0002388367,0.001024143],"genre_scores_gemma":[0.9987431,0.0002734585,0.0000620322,0.0000302903,0.0001344481,0.0001426113,0.0001026831,0.00005993603,0.0004515116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1237019,"threshold_uncertainty_score":0.9996984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04524792509087351,"score_gpt":0.3176504852980673,"score_spread":0.2724025602071938,"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."}}