{"id":"W4389662905","doi":"10.1016/j.trpro.2023.11.395","title":"Steel furnace slag aggregate for railway ballast: assessment of abrasion evolution by close-range photogrammetry","year":2023,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Research and Development in Structures and Construction; European Regional Development Fund; Fundação para a Ciência e a Tecnologia; Ministério da Ciência, Tecnologia e Ensino Superior; Saskatchewan Pulse Growers","keywords":"Ballast; Aggregate (composite); Slag (welding); Abrasion (mechanical); Electric arc furnace; Metallurgy; Environmental science; Materials science; Geotechnical engineering; Mining engineering; Geology; Composite material","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007603312,0.0001547498,0.0001994661,0.0003435771,0.0001188552,0.00002461613,0.000174945,0.0001324118,0.0000205095],"category_scores_gemma":[0.00005367988,0.0001522812,0.00008606621,0.0009181345,0.00006211326,0.0001931535,0.000005168115,0.0003205724,0.00001098155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000145415,"about_ca_system_score_gemma":0.0000875046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008506545,"about_ca_topic_score_gemma":0.0001737872,"domain_scores_codex":[0.9981459,0.00002416441,0.0003561477,0.0002453876,0.0006557223,0.0005726287],"domain_scores_gemma":[0.9991467,0.0001286309,0.00005532114,0.0001631436,0.0003979836,0.0001082438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000190279,0.00009908458,0.0498706,0.004639906,0.0001836282,0.00001900913,0.00312045,0.01120946,0.8482918,0.005643012,0.04876447,0.02796829],"study_design_scores_gemma":[0.003164055,0.0004927008,0.79024,0.0006070784,0.0000536829,0.000001254622,0.0025914,0.06469182,0.1145513,0.002576728,0.02040611,0.0006238974],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623489,0.0002376723,0.03431711,0.0000772276,0.0004916184,0.001216533,0.0002696165,0.0005324172,0.000508924],"genre_scores_gemma":[0.9968219,0.0002923872,0.001794432,0.000004547006,0.0001454979,0.0003771766,0.0002597704,0.00006756187,0.0002367231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7403694,"threshold_uncertainty_score":0.6209848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02622478023342928,"score_gpt":0.341975578600723,"score_spread":0.3157507983672937,"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."}}