{"id":"W4404252342","doi":"10.1177/03611981241284616","title":"Modeling the Time-Dependent Variation of Road Salt Concentrations Using Analytical and Machine-Learning Approaches to Advance Service Life Predictions for Concrete Structures","year":2024,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Concrete Corrosion and Durability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université Laval","funders":"","keywords":"Salting; Chloride; Corrosion; Service life; Environmental science; Durability; Computer science; Materials science; Chemistry; Composite material; Metallurgy","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":[0.0003944515,0.0005278833,0.0003258103,0.0004163538,0.0002029926,0.0005331384,0.0005240658,0.0007497261,0.0005949252],"category_scores_gemma":[0.001364264,0.0003357627,0.0004762246,0.0002572896,0.0002841298,0.0005254571,0.0002328466,0.0004966595,0.0001394993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007860992,"about_ca_system_score_gemma":0.0008436081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02546092,"about_ca_topic_score_gemma":0.01988236,"domain_scores_codex":[0.9999055,0.0000235427,0.000006834187,0.00002569145,0.00002063087,0.00001784055],"domain_scores_gemma":[0.9995609,0.000248652,0.00007942198,0.00001991149,0.0000683825,0.00002263565],"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":[0.000007146321,0.00002302871,0.001431155,0.000007656835,0.000005841323,0.00001283404,0.000008872797,0.9949576,0.0005538989,0.0002673858,0.00003884207,0.002685641],"study_design_scores_gemma":[3.087358e-7,0.000003065937,0.0001618928,4.591585e-7,7.073971e-7,0.000001088009,0.000001455242,0.999607,0.000102996,0.0001000193,0.00002022062,8.395198e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6605684,0.0003369073,0.3351884,0.0002947771,0.00003901227,0.00005828143,0.0002421798,0.000432756,0.002839307],"genre_scores_gemma":[0.9872549,0.00009282416,0.01159291,0.00001266951,0.000009302581,0.00002564504,0.0001181107,0.00001445591,0.0008792396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02546092,"threshold_uncertainty_score":0.05062544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1334080369069117,"score_gpt":0.3462460198872573,"score_spread":0.2128379829803456,"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."}}