{"id":"W2258598511","doi":"","title":"Local Calibration of the MEPDG Rutting Models for Ontario’s Flexible Roads: Recent Findings","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rut; Calibration; Scale (ratio); Set (abstract data type); Residual; Engineering; Environmental science; Civil engineering; Transport engineering; Computer science; Statistics; Mathematics; Geography; Cartography; Asphalt; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006760116,0.0004022427,0.0004743644,0.0008375824,0.0007695647,0.00009015539,0.0008110165,0.0003412449,0.0004596028],"category_scores_gemma":[0.000287985,0.0003035632,0.000262172,0.001783292,0.0006876235,0.0017773,0.00001782183,0.000928673,0.00002206432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009533875,"about_ca_system_score_gemma":0.0009113293,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007761636,"about_ca_topic_score_gemma":0.07297333,"domain_scores_codex":[0.9916143,0.0004809881,0.001558408,0.0007639944,0.00404559,0.001536658],"domain_scores_gemma":[0.99423,0.001050977,0.0001903352,0.0006634821,0.003510547,0.0003545973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002788415,0.0005675152,0.1047783,0.00272294,0.0005213482,0.00001468773,0.04288496,0.644219,0.1021658,0.02757007,0.02339337,0.0483736],"study_design_scores_gemma":[0.009803393,0.001743725,0.3475028,0.002652621,0.0002055131,8.54268e-7,0.01258614,0.1913876,0.3807165,0.02065094,0.03095147,0.00179838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8309053,0.0001373999,0.1609788,0.001801514,0.0004376024,0.003755732,0.0004869291,0.0003302427,0.001166473],"genre_scores_gemma":[0.9932594,0.0003806038,0.003208186,0.00003703682,0.000128437,0.001114799,0.0002533362,0.0001257732,0.001492408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4528314,"threshold_uncertainty_score":0.9999416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08189052311095182,"score_gpt":0.3524772298086427,"score_spread":0.2705867066976908,"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."}}