{"id":"W3083127234","doi":"10.1051/0004-6361/202039339","title":"Pulsars with NenuFAR: Backend and pipelines","year":2021,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"Centre National de la Recherche Scientifique; Institut National de Physique Nucléaire et de Physique des Particules; Agence Nationale de la Recherche; Conseil Régional, Île-de-France; Observatoire de Paris, Université de Recherche Paris Sciences et Lettres; Université d'Orléans; Centre National d’Etudes Spatiales","keywords":"Pulsar; Millisecond pulsar; Radio telescope; Instrumentation (computer programming); Telescope; Pipeline (software); Interstellar medium; Millisecond; High dynamic range; Range (aeronautics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001155361,0.001783799,0.0008104263,0.001473306,0.0005337325,0.002103711,0.002198124,0.0007065537,0.011064],"category_scores_gemma":[0.00255076,0.0007175807,0.0006453909,0.0007860647,0.0005024921,0.001738448,0.002300551,0.001643769,0.01009716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007724881,"about_ca_system_score_gemma":0.0008667966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002127952,"about_ca_topic_score_gemma":0.001273458,"domain_scores_codex":[0.9989687,0.00006214093,0.00005808628,0.0003783555,0.0003549839,0.0001777367],"domain_scores_gemma":[0.99892,0.0002137465,0.0001090141,0.0003466358,0.0002222796,0.000188383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007850911,0.0009723863,0.04380053,0.001814233,0.0007595551,0.002163333,0.001942203,0.01408088,0.1323839,0.01225209,0.2305252,0.5514548],"study_design_scores_gemma":[0.000738889,0.001643521,0.05330259,0.0003676962,0.0004325798,0.00229289,0.000284949,0.1386123,0.2534488,0.009837461,0.5384948,0.0005435659],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.1001496,0.00284729,0.2980275,0.0008970211,0.0007154149,0.001059831,0.02056126,0.5448214,0.03092063],"genre_scores_gemma":[0.4603601,0.001810221,0.3547345,0.00173426,0.00061843,0.001703144,0.1316745,0.02590201,0.02146285],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.011064,"threshold_uncertainty_score":0.03701276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007582217173093949,"score_gpt":0.2641611299937226,"score_spread":0.2565789128206287,"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."}}