{"id":"W4394593974","doi":"10.2139/ssrn.4789081","title":"Prediction of the Fundamental Viscoelasticity of Asphalt Mixtures Using Ml Algorithms","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Asphalt; Viscoelasticity; Algorithm; Computer science; Materials science; Composite material","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":[0.00108741,0.0009314828,0.0007583936,0.001456805,0.0003006063,0.001108335,0.0007943189,0.001248548,0.00137846],"category_scores_gemma":[0.004663068,0.0003936093,0.0005997342,0.0007735743,0.0004614352,0.002043412,0.0009113394,0.001118048,0.0006891487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005015619,"about_ca_system_score_gemma":0.0005703521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002019642,"about_ca_topic_score_gemma":0.001266158,"domain_scores_codex":[0.999725,0.00007759713,0.00001787086,0.00007174686,0.00007423779,0.00003343731],"domain_scores_gemma":[0.997652,0.001661583,0.0001918158,0.0001615535,0.0002629626,0.00007015582],"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":[0.0001622761,0.00009639529,0.001504904,0.00005158246,0.00002885702,0.00001872306,0.00002445998,0.9187602,0.00843404,0.001516764,0.0002578658,0.06914388],"study_design_scores_gemma":[0.00000243614,0.00001190641,0.0001456689,0.00000126909,0.000002062047,0.000001805392,0.000001552134,0.9980533,0.001317959,0.0004153676,0.00004469077,0.000002008578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1494501,0.0002584092,0.8474767,0.0001224187,0.00002938069,0.00003271869,0.0001587069,0.001310967,0.001160661],"genre_scores_gemma":[0.8466434,0.0001640409,0.1509482,0.00004045583,0.00004034883,0.00008101748,0.0003763639,0.0001411073,0.00156505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002019642,"threshold_uncertainty_score":0.005750835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253220567329238,"score_gpt":0.2610279568073942,"score_spread":0.2384957511341018,"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."}}