{"id":"W1150244867","doi":"10.1016/j.polymertesting.2015.08.010","title":"Prediction of melt rheological properties from GPC molecular weights","year":2015,"lang":"en","type":"article","venue":"Polymer Testing","topic":"Rheology and Fluid Dynamics Studies","field":"Chemical Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nova Chemicals (Canada)","funders":"NOVA Chemicals","keywords":"Materials science; Rheology; Gel permeation chromatography; Polymer; Molar mass distribution; Viscosity; Polymerization; Polyethylene; Polymer chemistry; 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":[0.000340736,0.000793317,0.0004468863,0.001176866,0.0002068116,0.0005029181,0.0002492309,0.0005837253,0.0008340135],"category_scores_gemma":[0.001691967,0.00032334,0.0005147257,0.0005061279,0.0002015419,0.0006512776,0.0002328395,0.0007477978,0.0007794979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002887653,"about_ca_system_score_gemma":0.0002237029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001622621,"about_ca_topic_score_gemma":0.001101825,"domain_scores_codex":[0.9998424,0.00002090433,0.000009663794,0.0000405685,0.00006822703,0.00001826095],"domain_scores_gemma":[0.9993742,0.0003330062,0.00009287851,0.00005644896,0.0001210656,0.00002241666],"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":[0.0003285203,0.0001190916,0.009620802,0.00006679076,0.00003307733,0.00008885172,0.00004907182,0.0197416,0.9413065,0.0001868996,0.0001528168,0.02830583],"study_design_scores_gemma":[0.00001306088,0.0003100724,0.01548294,0.000008840228,0.0000511999,0.00008368718,0.00001497495,0.2274469,0.7558721,0.0002236243,0.0004733776,0.00001923849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.930519,0.0004809178,0.06615941,0.00005536108,0.00001616238,0.00006390857,0.0005124569,0.0006347233,0.001557929],"genre_scores_gemma":[0.9899888,0.0002792843,0.008686162,0.0000120116,0.000007250081,0.00003237856,0.0003645643,0.00007752419,0.000551928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001622621,"threshold_uncertainty_score":0.00322634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0659760870899318,"score_gpt":0.2192611288469052,"score_spread":0.1532850417569734,"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."}}