{"id":"W4393627574","doi":"10.5281/zenodo.3819585","title":"Radio Frequency Interference (RFI)","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Interference (communication); Electromagnetic interference; Co-channel interference; Telecommunications; Computer science; Channel (broadcasting)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001915552,0.0002723106,0.0002493092,0.000244201,0.0006493835,0.0006523184,0.001333176,0.0001880143,0.0145068],"category_scores_gemma":[0.0003165621,0.0003051164,0.00006714236,0.0005049949,0.00009571981,0.0002034897,0.0005249661,0.0006445099,0.03147786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001876445,"about_ca_system_score_gemma":0.000003911429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001034913,"about_ca_topic_score_gemma":3.704798e-7,"domain_scores_codex":[0.9985083,0.000154149,0.0003140119,0.0003967537,0.0002796113,0.000347229],"domain_scores_gemma":[0.9989401,0.00001502276,0.00007726981,0.0005049619,0.0002500092,0.0002126468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001022149,0.00002730178,3.612184e-8,0.0001965402,0.00005141429,0.00002680816,0.0001199597,0.0003036071,0.0003606713,0.0001068933,0.9946257,0.004170862],"study_design_scores_gemma":[0.000197446,0.0001180592,0.000006772509,0.00007608709,0.00002900305,0.0000533536,0.00003709708,0.00313328,0.00003501313,0.00004175099,0.9959628,0.0003093114],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000005937484,0.000213441,0.02743078,0.000124877,0.0003418744,0.0003886754,0.9568039,0.001826312,0.01286419],"genre_scores_gemma":[0.001096723,0.00115343,0.0003242209,0.0001049705,0.0002830701,4.985524e-8,0.9953898,0.001516501,0.0001311935],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03858593,"threshold_uncertainty_score":0.9999401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02985502469437441,"score_gpt":0.2148023207702976,"score_spread":0.1849472960759232,"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."}}