{"id":"W2902809416","doi":"10.22323/1.309.0027","title":"4th dimensional tracking: the GigaTracker of NA62 experiment.","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"TRIUMF","funders":"Istituto Nazionale di Fisica Nucleare; Fonds De La Recherche Scientifique - FNRS; CERN","keywords":"Physics; Large Hadron Collider; Tracking (education); Detector; Beam (structure); Nuclear physics; Silicon; Planar; Pixel; Optics; Optoelectronics; Computer science; Computer graphics (images)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001700588,0.0001850366,0.0001990873,0.00002630658,0.00008838207,0.0000378249,0.0002892605,0.00005933177,0.00361556],"category_scores_gemma":[0.000001744556,0.0001176715,0.0001287304,0.0000571938,0.00009374359,0.00005659474,0.0003034686,0.0002365581,0.0001281451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001055771,"about_ca_system_score_gemma":0.0001066943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008280125,"about_ca_topic_score_gemma":0.000001145391,"domain_scores_codex":[0.9990311,0.00002634506,0.0002859175,0.0002285481,0.0002269441,0.0002011225],"domain_scores_gemma":[0.9992986,0.00003776332,0.0001621543,0.0003600838,0.00009601128,0.00004533957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003729736,0.001478461,0.7076437,0.0002777036,0.002720012,0.000004055667,0.01467564,0.001925414,0.07977452,0.03402602,0.1059636,0.05113797],"study_design_scores_gemma":[0.0004580047,0.00004503862,0.04584745,0.000112278,0.00004919083,5.230038e-7,0.0001813375,0.004837745,0.9335517,0.00514092,0.00928323,0.0004925862],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814793,0.00007200695,0.0004197007,0.0001026312,0.0005342869,0.0001863607,0.00002002575,0.00002129446,0.01716437],"genre_scores_gemma":[0.9977162,0.000001018548,0.0006061919,0.00003654091,0.0004298089,0.00003891709,0.00003832709,0.00001426485,0.001118723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8537772,"threshold_uncertainty_score":0.9972953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02940914582020031,"score_gpt":0.280820180193602,"score_spread":0.2514110343734017,"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."}}