{"id":"W2948274948","doi":"10.11159/iccste19.187","title":"Fiber interferometric system for vehicle monitoring near railway level crossings","year":2019,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Civil, Structural and Transportation Engineering","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerstvo Průmyslu a Obchodu; Ministerstvo Školství, Mládeže a Tělovýchovy","keywords":"Interferometry; Optical fiber; Computer science; Optics; Telecommunications; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002260107,0.0004052938,0.0002978079,0.0007298242,0.0002473696,0.0002089016,0.0004424017,0.0004308466,0.001682982],"category_scores_gemma":[0.0002197362,0.0001234289,0.0001516052,0.0004999788,0.0001315797,0.0004483304,0.0003052567,0.0003006633,0.0006581819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003166032,"about_ca_system_score_gemma":0.0003513719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001096273,"about_ca_topic_score_gemma":0.001993252,"domain_scores_codex":[0.9997171,0.00004140841,0.000008184526,0.00006972216,0.0001301968,0.00003338524],"domain_scores_gemma":[0.9998785,0.00001601054,0.00002456271,0.00001378358,0.00005592603,0.00001118602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002835162,0.0001168331,0.01285871,0.0001820421,0.00004216212,0.0001542335,0.0001769247,0.003902796,0.7391205,0.0009984027,0.001797605,0.2403662],"study_design_scores_gemma":[0.00007920125,0.002084919,0.1014346,0.0000648966,0.0002167687,0.001601725,0.000295601,0.1928419,0.6624693,0.001212887,0.03756143,0.0001367375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4656073,0.001262448,0.5156791,0.0002485359,0.0001903092,0.000301789,0.00107421,0.006370632,0.009265719],"genre_scores_gemma":[0.817507,0.0005080202,0.176505,0.0001266711,0.00009638558,0.0001410876,0.0007223027,0.00005087938,0.004342569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001682982,"threshold_uncertainty_score":0.005630076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02058735568406983,"score_gpt":0.2306381640364415,"score_spread":0.2100508083523717,"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."}}