{"id":"W4399784213","doi":"10.25518/0037-9565.11895","title":"Automated Transient Detection in the Context of the 4m ILMT","year":2024,"lang":"en","type":"article","venue":"Bulletin de la Société Royale des Sciences de Liège","topic":"Advanced Electrical Measurement Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Service Public de Wallonie; Université de Liège; Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS; Department of Science and Technology, Ministry of Science and Technology, India; York University","keywords":"Transient (computer programming); Context (archaeology); Transient analysis; Computer science; Engineering; Geology; Transient response; Electrical engineering","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":[0.001286218,0.0001096698,0.0001080087,0.00006729234,0.0001542581,0.000078765,0.0004319096,0.00008708671,0.00004296545],"category_scores_gemma":[0.0001023886,0.00006991263,0.00008015185,0.0007086672,0.0005949727,0.00004206791,0.00001779917,0.0002810123,0.000006514808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000256642,"about_ca_system_score_gemma":0.00004198915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008575919,"about_ca_topic_score_gemma":0.00004603226,"domain_scores_codex":[0.9988493,0.0002188557,0.0001922647,0.0001533555,0.00028114,0.0003050297],"domain_scores_gemma":[0.9993749,0.0004332335,0.0000214094,0.0001277752,0.00001967968,0.00002305175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004435498,0.0003062346,0.002958891,0.001014418,0.0001114014,0.00006823877,0.02573543,0.06915344,0.4027497,0.01346496,0.04909359,0.4352993],"study_design_scores_gemma":[0.0005110266,0.0003565399,0.01637874,0.0006199036,0.00006764834,0.0001141632,0.002539724,0.4738069,0.3871089,0.01396324,0.1039525,0.0005808356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9110951,0.006046872,0.0499354,0.001928701,0.0004097104,0.0008197883,0.000008871713,0.002609381,0.02714614],"genre_scores_gemma":[0.9978577,0.00007565206,0.001701688,0.000220301,0.00002688317,0.00006699829,1.695611e-7,0.00001384133,0.00003675413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4347185,"threshold_uncertainty_score":0.2850955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053920650478341,"score_gpt":0.2959603285618999,"score_spread":0.2754211220571165,"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."}}