{"id":"W4399593875","doi":"10.1016/j.compositesb.2024.111623","title":"Surface-engineered in-situ fibrillated thermoplastic polyurethane as toughening reinforcement for geopolymer-based mortar","year":2024,"lang":"en","type":"article","venue":"Composites Part B Engineering","topic":"Concrete and Cement Materials Research","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Materials science; Toughening; Composite material; Geopolymer; Reinforcement; Polyurethane; Mortar; Toughness; Compressive strength","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"],"consensus_categories":[],"category_scores_codex":[0.0001980096,0.0003591327,0.0003552725,0.0002954805,0.00004567866,0.0002035575,0.0001909155,0.000120342,0.0001753952],"category_scores_gemma":[0.00002930239,0.000385299,0.0001230664,0.0004201961,0.00001592119,0.0001670625,0.00004560783,0.0002125185,0.0000764451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001560407,"about_ca_system_score_gemma":0.00004442215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002112559,"about_ca_topic_score_gemma":0.000004060444,"domain_scores_codex":[0.9982635,0.00001304115,0.0004460878,0.0003138424,0.0002584412,0.0007051366],"domain_scores_gemma":[0.9992177,0.0003200423,0.00001517256,0.0002699987,0.00003178293,0.0001452549],"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":[0.00002133119,0.000002705528,0.00002043643,0.0004362306,0.00007119976,0.00002424689,0.00006655602,0.5298656,0.4689658,0.0001416934,0.000192185,0.0001920008],"study_design_scores_gemma":[0.0003585875,0.00007250981,0.00004049899,0.0003741489,0.00002244657,0.000006184556,0.000006844314,0.6190154,0.3739047,0.000004936004,0.005904145,0.0002895819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9593007,0.004439598,0.03227633,0.00005474104,0.001260105,0.0006081382,0.00002645131,0.001122191,0.0009117203],"genre_scores_gemma":[0.9983836,0.00007432381,0.0007347037,0.00001460565,0.0002327182,0.00008852021,0.0001717186,0.0001266317,0.0001732059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09506109,"threshold_uncertainty_score":0.9998599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01449626902466589,"score_gpt":0.2458928746564598,"score_spread":0.231396605631794,"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."}}