{"id":"W1963091146","doi":"10.1002/app.42438","title":"Polypropylene reinforced with nanocrystalline cellulose: Coupling agent optimization","year":2015,"lang":"en","type":"article","venue":"Journal of Applied Polymer Science","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"FPInnovations; CRB Innovations (Canada); Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Karlsruhe Institute of Technology; Fonds Québécois de la Recherche sur la Nature et les Technologies; Ministero dello Sviluppo Economico; FPInnovations; ArboraNano","keywords":"Materials science; Polypropylene; Composite material; Maleic anhydride; Rheology; Nanocrystalline material; Cellulose; Nanocomposite; Polymer; Copolymer; Chemical engineering; Nanotechnology","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":[],"consensus_categories":[],"category_scores_codex":[0.001829224,0.0001984023,0.0003274425,0.000301794,0.0002708782,0.0001776194,0.0007801199,0.00003866109,0.00007640252],"category_scores_gemma":[0.00006397852,0.0001337501,0.00004228852,0.0009738129,0.0008985904,0.0007969532,0.0002275466,0.0002015443,0.00002746645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002680756,"about_ca_system_score_gemma":0.001025425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001010922,"about_ca_topic_score_gemma":5.677426e-7,"domain_scores_codex":[0.9965751,0.00001368679,0.0005497402,0.0003137357,0.001950629,0.0005971316],"domain_scores_gemma":[0.9977421,0.00006039303,0.0006185894,0.0002997991,0.000759082,0.0005200822],"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":[0.0003318405,0.00002814813,0.000008050294,0.00001106916,0.000007946315,0.00003081849,0.000509477,0.2072547,0.7902257,0.001104996,0.00006780752,0.0004194142],"study_design_scores_gemma":[0.0008965195,0.000325389,0.000007790112,0.00004237497,0.00001852173,0.00006033283,0.0006897519,0.02039023,0.9771174,0.00006038813,0.0002032469,0.0001880094],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8177171,0.002083292,0.1732848,0.0003887258,0.0005477638,0.0003152683,0.000003665161,0.00005968308,0.005599725],"genre_scores_gemma":[0.961231,0.00007507821,0.03788744,0.00007696897,0.0001830224,0.000006565694,7.626236e-7,0.00001945595,0.0005197209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1868917,"threshold_uncertainty_score":0.5454171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02284945547991525,"score_gpt":0.2720315754647871,"score_spread":0.2491821199848719,"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."}}