{"id":"W2011506992","doi":"10.1002/app.23046","title":"Differential scanning calorimetry of copolymer of isotactic polypropylene backbone with grafted poly(ethylene‐<i>co</i>‐propylene) branches","year":2006,"lang":"en","type":"article","venue":"Journal of Applied Polymer Science","topic":"Polymer crystallization and properties","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Differential scanning calorimetry; Tacticity; Materials science; Polymer chemistry; Enthalpy of fusion; Polypropylene; Melting point; Copolymer; Crystallization; Enthalpy; Electron paramagnetic resonance; Ethylene propylene rubber; Ethylene; Polymer; Analytical Chemistry (journal); Chemistry; Polymerization; Composite material; Thermodynamics; Chromatography; Organic chemistry; Nuclear magnetic resonance","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":[0.0007178686,0.0003603418,0.000796399,0.0006993355,0.0002872947,0.0001242007,0.001020813,0.0001095838,0.000547152],"category_scores_gemma":[0.00001891885,0.0002540484,0.0001496457,0.001254929,0.00227877,0.0008566092,0.000131498,0.0002425438,0.00001104295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007851061,"about_ca_system_score_gemma":0.0009325956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004328357,"about_ca_topic_score_gemma":0.000008953431,"domain_scores_codex":[0.9957537,0.00007496862,0.001317104,0.0004289762,0.001773222,0.0006520281],"domain_scores_gemma":[0.9970831,0.0001017653,0.00168404,0.0004743665,0.0003894699,0.0002672793],"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":[0.0008605245,0.0002421365,0.001401871,0.00007810822,0.00002877296,0.000005736479,0.0005437521,0.00002470532,0.9935348,0.001635617,0.000051321,0.001592656],"study_design_scores_gemma":[0.001221436,0.00039431,0.002516089,0.0001444107,0.00009444082,0.00006626922,0.0003507494,0.00005228289,0.9946532,0.0001232992,0.0000747813,0.0003087944],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884692,0.005211098,0.003471289,0.0001315465,0.0004394274,0.0001605116,0.00002828749,0.00003033642,0.002058292],"genre_scores_gemma":[0.9975641,0.00003389249,0.001827432,0.0001136067,0.0002159193,0.000004330841,0.000002218659,0.0000374121,0.0002011303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009094845,"threshold_uncertainty_score":0.9999912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007556139090074358,"score_gpt":0.2210991089075277,"score_spread":0.2135429698174534,"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."}}