{"id":"W2407316078","doi":"10.1021/acs.macromol.5b02235","title":"Phase Diagram of Rod–Coil Diblock Copolymer Melts","year":2015,"lang":"en","type":"article","venue":"Macromolecules","topic":"Block Copolymer Self-Assembly","field":"Materials Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ministry of Science and Technology of the People's Republic of China; Ministry of Education of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Gyroid; Spinodal; Phase diagram; Isotropy; Lamellar structure; Materials science; Copolymer; Phase (matter); Curvature; Frustration; Condensed matter physics; Volume fraction; Lamellar phase; Thermodynamics; Polymer; Physics; Geometry; Optics; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001367891,0.00023105,0.0003377649,0.001181575,0.0002231737,0.0003074618,0.0002317052,0.0002858221,0.001609678],"category_scores_gemma":[0.0003699275,0.0001772051,0.0001361965,0.0004282023,0.0003055085,0.0004201364,0.0001344719,0.0003617671,0.0001898443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004840481,"about_ca_system_score_gemma":0.0002464574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006544901,"about_ca_topic_score_gemma":0.000583655,"domain_scores_codex":[0.9998851,0.0000119014,0.000007724356,0.00002712374,0.00004716487,0.00002098264],"domain_scores_gemma":[0.9998285,0.00005245347,0.00004320185,0.000008267894,0.00004770289,0.00001996033],"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":[0.0003771265,0.00005422724,0.001610856,0.0001226639,0.00001207444,0.0001380211,0.0001637138,0.009494849,0.9716223,0.008074476,0.0003300201,0.007999754],"study_design_scores_gemma":[0.0000792155,0.0003831772,0.01663393,0.00003600264,0.00002462328,0.0002252241,0.0000648529,0.3174893,0.6569415,0.003879784,0.004203053,0.00003940361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752752,0.001458262,0.01748105,0.00007900171,0.0000123855,0.00004501867,0.000566316,0.0005444391,0.004538234],"genre_scores_gemma":[0.9940162,0.0001919729,0.004554219,0.00001494373,0.000006376045,0.00003636693,0.0003976684,0.00004117888,0.0007409827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001609678,"threshold_uncertainty_score":0.005384922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149043732741693,"score_gpt":0.2831036667837987,"score_spread":0.2616132294563818,"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."}}