{"id":"W3005422800","doi":"10.1002/pen.25352","title":"Dielectric Relaxation Dynamics of Clay‐Containing Low‐Density polyethylene Blends and Nanocomposites","year":2020,"lang":"en","type":"article","venue":"Polymer Engineering and Science","topic":"Polymer Nanocomposites and Properties","field":"Materials Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Low-density polyethylene; Nanocomposite; Dielectric; Polyethylene; Polystyrene; Composite material; Relaxation (psychology); Dielectric loss; Dispersion (optics); Polymer","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":[0.0002452819,0.0001345182,0.0001969607,0.0001122556,0.0001719975,0.00009625425,0.0001893311,0.00003879274,0.000008518976],"category_scores_gemma":[0.00006038446,0.0001175434,0.00002176544,0.0004364319,0.0002251917,0.0003093518,0.000139807,0.00008621155,0.000002203806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002045702,"about_ca_system_score_gemma":0.00005346459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001508732,"about_ca_topic_score_gemma":0.000002349841,"domain_scores_codex":[0.9989526,0.00001555241,0.0002039118,0.0003147439,0.0002197948,0.0002934411],"domain_scores_gemma":[0.999535,0.00006013017,0.00007716082,0.0001153432,0.00003921684,0.0001731563],"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":[0.00002239096,0.000005618621,0.00147454,0.00004679217,0.0000028099,0.000001353231,0.001009117,0.0001305429,0.992439,0.00142964,9.457166e-7,0.003437228],"study_design_scores_gemma":[0.0001220544,0.0001446599,0.002198597,0.00003507455,0.00001077357,0.00001346713,0.00004324851,0.1196423,0.8776361,0.00000992035,0.000007663164,0.0001360741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933386,0.004192803,0.00160468,0.0004642225,0.0001084866,0.00006179883,0.000006555985,0.00007277005,0.0001500319],"genre_scores_gemma":[0.9990458,0.00008545852,0.0007082126,0.00008764899,0.00003581453,0.000003028524,0.000001252683,0.000009625302,0.00002313442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1195118,"threshold_uncertainty_score":0.4793282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007497479996375916,"score_gpt":0.1952442367896946,"score_spread":0.1877467567933187,"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."}}