{"id":"W4308727541","doi":"10.1520/jte20220288","title":"Comparison of Parameters from a New MSCR Approach with Classical MSCR and LAS Parameters for Simplified Binder Selection","year":2022,"lang":"en","type":"article","venue":"Journal of Testing and Evaluation","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Asphalt; Fatigue cracking; Materials science; Deformation (meteorology); Material selection; Rut; Fracture (geology); Selection (genetic algorithm); Creep; Cracking; Index (typography); Stress (linguistics); Composite material; Structural engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001613679,0.0007496704,0.0006123906,0.002842511,0.0001579735,0.0005693899,0.0008649662,0.0006995121,0.002009247],"category_scores_gemma":[0.004123081,0.0002046866,0.0004981694,0.001136723,0.0003008652,0.001024124,0.0005032216,0.0003463277,0.0007944768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004428033,"about_ca_system_score_gemma":0.0003517941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001030941,"about_ca_topic_score_gemma":0.001788283,"domain_scores_codex":[0.9977688,0.0003488471,0.0001770429,0.0002839382,0.001335853,0.00008567573],"domain_scores_gemma":[0.9962509,0.001319616,0.0005105595,0.0004673523,0.001362745,0.00008878838],"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":[0.001168472,0.000432163,0.02527023,0.0007087212,0.0001071829,0.0001743334,0.0003313581,0.02246009,0.6974247,0.0007371692,0.0006727267,0.2505129],"study_design_scores_gemma":[0.00006490396,0.003871341,0.0878005,0.00006939637,0.0002268205,0.0007366484,0.0004319787,0.1773338,0.7219517,0.0006925453,0.006633874,0.000186483],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8268671,0.001873097,0.1615681,0.0001133451,0.00007137276,0.0002591102,0.00080698,0.002427844,0.006013113],"genre_scores_gemma":[0.9647319,0.0002271427,0.03337622,0.00002837775,0.00001585035,0.00008108398,0.0004539243,0.0001166688,0.000968922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002842511,"threshold_uncertainty_score":0.008534014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1330204950983816,"score_gpt":0.3324053257192606,"score_spread":0.199384830620879,"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."}}