{"id":"W4251729664","doi":"10.1520/jte20170682","title":"Superpave Design Aggregate Structure Considering Uncertainty: I. Selection of Trial Blends","year":2018,"lang":"en","type":"article","venue":"Journal of Testing and Evaluation","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Aggregate (composite); Selection (genetic algorithm); Materials science; Structural engineering; Composite material; Computer science; Engineering; Artificial intelligence","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":[0.0009942271,0.00007960575,0.0001821442,0.0001477512,0.00004285416,0.00002322955,0.00003953534,0.00005971141,0.00003088181],"category_scores_gemma":[0.001313147,0.00006809358,0.00004019807,0.0002510343,0.00002565814,0.0001008619,0.000005758168,0.0001437999,4.138719e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004287318,"about_ca_system_score_gemma":0.00005368305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009551473,"about_ca_topic_score_gemma":0.000002048102,"domain_scores_codex":[0.9992093,0.00005025664,0.0003429555,0.00005959305,0.0002426597,0.00009523655],"domain_scores_gemma":[0.9991257,0.0001999103,0.0001372136,0.00004943543,0.0004458625,0.00004194249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001182274,0.000004080233,0.00006385893,0.00002216743,0.0000542667,4.867217e-7,0.00007994102,0.8850046,0.06861094,0.000002223694,0.00004339168,0.04599579],"study_design_scores_gemma":[0.002546729,0.0004779857,0.0002152031,0.0001881097,0.0001775688,0.00005619493,0.00003042712,0.979282,0.01602086,0.0009198441,0.00001419948,0.00007088763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621211,0.0002270502,0.03723669,0.00002417351,0.000239768,0.00007029585,7.487025e-7,0.00003556068,0.00004465835],"genre_scores_gemma":[0.9863116,0.00001897613,0.01334103,0.000002903811,0.00031185,9.45045e-7,5.012163e-7,0.000009086173,0.000003082976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09427736,"threshold_uncertainty_score":0.2776776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04764946333560502,"score_gpt":0.2765535715031338,"score_spread":0.2289041081675288,"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."}}