{"id":"W2176726395","doi":"10.1109/rtc.2007.4382848","title":"The TIGRESS DAQ/Trigger system","year":2007,"lang":"en","type":"article","venue":"","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Data acquisition; Detector; Computer hardware; Computer science; Nuclear electronics; SIGNAL (programming language); Telecommunications","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":[0.0003215599,0.00005630404,0.00004726305,0.00001205176,0.00021976,0.0000406873,0.0001082246,0.00001030135,0.0001234503],"category_scores_gemma":[7.338597e-7,0.00003246422,0.00002664904,0.00008821232,0.00002086983,0.00005541926,0.00002031991,0.0000506307,0.0003831884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001071159,"about_ca_system_score_gemma":0.0000129048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002768737,"about_ca_topic_score_gemma":0.00000729317,"domain_scores_codex":[0.9994857,0.000006561123,0.0001219705,0.00007058352,0.00009418048,0.000220955],"domain_scores_gemma":[0.999721,0.00006043865,0.00002799576,0.0001250974,0.0000221637,0.00004328677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005814038,0.00003240497,0.5037159,0.00001262282,0.00008941042,0.000003326966,0.0002967467,0.000007032756,0.001920637,0.2838956,0.006722782,0.2032454],"study_design_scores_gemma":[0.0006734866,0.00002963738,0.2290001,0.00003507426,0.00001754421,0.000002502087,0.002053825,0.002900437,0.3232346,0.0006092841,0.4409849,0.0004584922],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.806615,0.00003777523,0.006263392,0.00005504923,0.0002795323,0.00007120175,0.000001006711,0.00004768681,0.1866293],"genre_scores_gemma":[0.9951314,4.910991e-7,0.0001058599,0.00001203604,0.0001990662,0.000005159806,0.000001370851,0.000004369794,0.004540205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4342622,"threshold_uncertainty_score":0.492524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0082628300700948,"score_gpt":0.2327418814079717,"score_spread":0.2244790513378769,"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."}}