{"id":"W4313055198","doi":"10.1109/access.2022.3215972","title":"Audiogram Digitization Tool for Audiological Reports","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Workplace Safety and Insurance Board","keywords":"Audiogram; Computer science; Digitization; Adjudication; Annotation; Process (computing); Noise (video); Speech recognition; Artificial intelligence; Telecommunications; Hearing loss; Audiology; Medicine; Programming language; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009875366,0.00008137105,0.0001001462,0.00004613454,0.0001417975,0.00006300447,0.0001392432,0.00003033317,0.00004577335],"category_scores_gemma":[0.00002794586,0.00007720983,0.00005255596,0.0001348731,0.000009517292,0.0001712016,0.00004459075,0.0001019061,9.265063e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007216288,"about_ca_system_score_gemma":0.000008928088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003519787,"about_ca_topic_score_gemma":7.703333e-7,"domain_scores_codex":[0.9993978,0.000007198613,0.0001623578,0.0001339585,0.00009906987,0.000199585],"domain_scores_gemma":[0.9997502,0.00002649009,0.00003299864,0.0001409507,0.00002818823,0.0000211707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002810962,0.00002119695,0.02571812,0.00008974488,0.00006132857,0.0001223194,0.0001489546,0.8586451,0.008220544,0.0005618225,0.06415585,0.04222685],"study_design_scores_gemma":[0.001364627,0.0003760919,0.1264934,0.00005380702,0.00009980985,0.0005849098,0.0001871782,0.03721632,0.09362024,0.02742912,0.7107726,0.001801893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8702785,0.00005928892,0.121593,0.00002944743,0.005506138,0.0003623109,0.00001734023,0.00047882,0.001675148],"genre_scores_gemma":[0.9985363,0.000007251694,0.0004411079,0.0000799876,0.0004827416,0.0003104644,0.00002687093,0.00001973463,0.00009548188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8214288,"threshold_uncertainty_score":0.3148525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421382121888545,"score_gpt":0.2603784318779602,"score_spread":0.2461646106590747,"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."}}