{"id":"W4408324274","doi":"10.1109/globecom52923.2024.10901287","title":"Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Spectrogram; Computer science; Training (meteorology); Artificial intelligence; Speech recognition","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":[0.0001653181,0.0002229551,0.0001741518,0.0001353522,0.00006800807,0.0002149311,0.0001453854,0.00008763099,0.0001575022],"category_scores_gemma":[0.00001829224,0.0002362951,0.0001308634,0.0001901491,0.0000150972,0.0003699081,0.0000254903,0.0003116851,0.0000511071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002126296,"about_ca_system_score_gemma":0.00003258045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002764713,"about_ca_topic_score_gemma":7.070918e-7,"domain_scores_codex":[0.9989141,0.0000106111,0.0002197331,0.0002686245,0.0001862469,0.000400733],"domain_scores_gemma":[0.9996165,0.0001421578,0.000008094935,0.0001183871,0.00002104158,0.00009384088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004400878,0.00001185031,0.00001796302,0.0003184156,0.0001710798,0.000004319537,0.004299807,0.9534001,0.006233107,0.02817482,0.0003958109,0.006968374],"study_design_scores_gemma":[0.0001957524,0.00006528001,0.00001297747,0.00004397105,0.00001855674,0.00001873413,0.0002852919,0.9429656,0.001778556,0.0006465141,0.05369542,0.0002733133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02031103,0.001591662,0.9396197,0.00008638508,0.001014901,0.0004454778,0.000004582272,0.008439125,0.02848719],"genre_scores_gemma":[0.8047192,0.00004567751,0.1936724,0.000009715059,0.000358585,0.0001561153,0.00005411314,0.0001230553,0.0008611967],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7844081,"threshold_uncertainty_score":0.9635837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02236221426586471,"score_gpt":0.2554517555380715,"score_spread":0.2330895412722068,"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."}}