{"id":"W2015980588","doi":"10.1021/ma020053w","title":"Phase Segregation in SAN/PMMA Blends Probed by Rheology, Microscopy, and Inverse Gas Chromatography Techniques","year":2002,"lang":"en","type":"article","venue":"Macromolecules","topic":"Polymer crystallization and properties","field":"Materials Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Inverse gas chromatography; Viscoelasticity; Lower critical solution temperature; Rheology; Materials science; Dynamic mechanical analysis; Optical microscope; Polymer chemistry; Methyl methacrylate; Poly(methyl methacrylate); Time–temperature superposition; Phase (matter); Polymer blend; Microscopy; Relaxation (psychology); Polymer; Thermodynamics; Composite material; Copolymer; Scanning electron microscope; Chemistry; Optics; Organic 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":[],"consensus_categories":[],"category_scores_codex":[0.0001499516,0.0001623659,0.0001727577,0.0001640064,0.00009680601,0.0001035195,0.0001417263,0.00009436368,0.0006968259],"category_scores_gemma":[0.00002170857,0.0001472017,0.00002804793,0.0002287011,0.0003046706,0.0001973391,0.00006478141,0.00007044202,0.00003221759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001738689,"about_ca_system_score_gemma":0.00001255478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001177219,"about_ca_topic_score_gemma":0.00008063877,"domain_scores_codex":[0.9989628,0.0001221438,0.0002432277,0.0002969303,0.0001153605,0.0002595321],"domain_scores_gemma":[0.9996402,0.00001522562,0.0000830833,0.0001647133,0.00002672442,0.00007008397],"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":[0.00002253245,0.0001204412,0.0005033684,0.00003645818,0.000003511175,0.00001107165,0.0006358431,3.589678e-7,0.9908616,0.0003679645,0.003393589,0.004043244],"study_design_scores_gemma":[0.0007520884,0.0001697799,0.00002217453,0.00004942688,0.000007354918,0.00002756101,0.00008734065,0.0008167408,0.9937757,0.0004888696,0.003613286,0.0001896609],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994532,0.001816643,0.001029234,0.0003703006,0.00005928669,0.0002447603,0.00004893745,0.0001867745,0.001712042],"genre_scores_gemma":[0.9958642,0.0003366536,0.002840001,0.0004683912,0.00001268139,0.00005662574,0.00002926704,0.00002002298,0.0003721213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003853583,"threshold_uncertainty_score":0.7629755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01244912897291891,"score_gpt":0.2473381258620019,"score_spread":0.234888996889083,"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."}}