{"id":"W2727265441","doi":"10.1016/j.ijrefrig.2017.06.035","title":"Rheology of ethylene- and propylene-glycol ice slurries: Experiments and ANN model","year":2017,"lang":"en","type":"article","venue":"International Journal of Refrigeration","topic":"Phase Change Materials Research","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rheology; Slurry; Materials science; Ethylene glycol; Dilatant; Shear thinning; Rheometer; Shear rate; Viscosity; Composite material; Chemical engineering; Engineering","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":[0.0002374015,0.00007158371,0.000135447,0.000132497,0.00005474193,0.0001166464,0.0001983966,0.00005461866,0.00002147387],"category_scores_gemma":[0.0001098929,0.00006389837,0.00001951836,0.00001011759,0.00007792258,0.0005042331,0.00006317896,0.00008472591,9.385682e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000440583,"about_ca_system_score_gemma":0.00002911888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001020297,"about_ca_topic_score_gemma":0.000007228396,"domain_scores_codex":[0.9993016,0.00002165721,0.0002666558,0.00006760392,0.0002630469,0.00007947901],"domain_scores_gemma":[0.999338,0.00002291952,0.0001790388,0.00009389676,0.0003172767,0.00004891771],"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":[0.0001713124,0.00003058336,0.0008617201,0.00004319719,0.0001309627,0.00002373288,0.001294432,0.002637293,0.9881806,0.001130717,0.0002193271,0.005276164],"study_design_scores_gemma":[0.0015266,0.0001738045,0.003016092,0.0001533889,0.00001453755,0.0001670668,0.0001039562,0.1976561,0.7951354,0.001496784,0.0004166334,0.0001395851],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903384,0.0003679578,0.007932542,0.0002760242,0.0004702913,0.00007203686,0.00001494463,0.000006917913,0.000520846],"genre_scores_gemma":[0.9975077,0.0005842585,0.001596916,0.00001408279,0.000179115,0.000004889759,0.000002854982,0.00001009386,0.000100079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1950188,"threshold_uncertainty_score":0.26057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04542064190516552,"score_gpt":0.3547705592430226,"score_spread":0.309349917337857,"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."}}