{"id":"W4285535022","doi":"10.1299/jsmetld.2021.30.ss2-4-1","title":"A Study of Long Rail Axial Force Monitoring Method Using Image Reading","year":2021,"lang":"en","type":"article","venue":"The Proceedings of the Transportation and Logistics Conference","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Displacement (psychology); Reliability (semiconductor); Track (disk drive); Structural engineering; Welding; Buckling; Computer science; Engineering; Mechanical engineering; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000180105,0.0001116301,0.0001799493,0.00003138353,0.00006577569,0.00003071496,0.000154459,0.0000404388,0.000002227752],"category_scores_gemma":[0.00005392177,0.00008223098,0.00003639623,0.0001840057,0.00005572346,0.00007612045,0.00001074797,0.0001484696,7.488531e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001247973,"about_ca_system_score_gemma":0.00001681938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003771158,"about_ca_topic_score_gemma":0.00001706007,"domain_scores_codex":[0.9993572,0.00000593133,0.0002606,0.0001098028,0.0001498107,0.0001166118],"domain_scores_gemma":[0.9995121,0.00006267583,0.00008215038,0.00008723951,0.0002291642,0.00002665768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005850349,0.000144314,0.06697456,0.001450324,0.0002812323,0.00001001666,0.0298346,0.3878762,0.4799067,0.03001525,0.000007466348,0.003440906],"study_design_scores_gemma":[0.001123533,0.0001090749,0.1953326,0.0005246763,0.0005509165,0.00002335975,0.01826029,0.6123214,0.1683836,0.002928772,0.000006162521,0.0004355806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8302251,0.00003856967,0.1691232,0.0000130784,0.0001312073,0.000109855,0.00001089711,0.00004039146,0.000307729],"genre_scores_gemma":[0.9890375,0.00004811474,0.01082416,0.000001558725,0.00002525255,0.000003960137,0.000001348463,0.0000154039,0.00004265782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3115231,"threshold_uncertainty_score":0.3353282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03206123553341357,"score_gpt":0.2692524443178588,"score_spread":0.2371912087844453,"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."}}