{"id":"W2111716386","doi":"10.3141/1778-18","title":"Use of Long-Term Pavement Performance Data for Calibration of Pavement Design Models","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Subgrade; Overlay; Pavement engineering; Calibration; Engineering; Asphalt pavement; Term (time); Civil engineering; Pavement management; Structural engineering; Asphalt; Geotechnical engineering; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005855325,0.0002359993,0.0004554401,0.0008200877,0.0002365436,0.00006186443,0.001165191,0.0001420483,0.0001405486],"category_scores_gemma":[0.0000684378,0.0001938095,0.0001942325,0.001232456,0.0002789721,0.002482078,0.00001211874,0.000718769,0.000001714228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002217532,"about_ca_system_score_gemma":0.0004043204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004953291,"about_ca_topic_score_gemma":0.002846869,"domain_scores_codex":[0.9932819,0.0004803155,0.001967337,0.0003159651,0.003309663,0.0006448296],"domain_scores_gemma":[0.9948902,0.0007723011,0.0005379707,0.0008345494,0.002778771,0.0001862388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001405869,0.0003740262,0.2094359,0.001665293,0.0003790336,0.00001200465,0.001755436,0.7395405,0.0258598,0.0004149153,0.002428947,0.01672821],"study_design_scores_gemma":[0.002256829,0.001340468,0.4882054,0.0008836481,0.0001446965,4.423756e-7,0.000397936,0.4471204,0.05816705,0.0006147486,0.0006069426,0.000261444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7472583,0.0001538335,0.2502569,0.0002021944,0.0002572256,0.001698121,0.0001463428,0.00001906481,0.000008016154],"genre_scores_gemma":[0.9863449,0.004198697,0.008817792,0.00001047772,0.0001019937,0.0001214781,0.0002080012,0.00006076372,0.0001358631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2924202,"threshold_uncertainty_score":0.7903324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.36071492478982,"score_gpt":0.4059596638405261,"score_spread":0.04524473905070614,"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."}}