{"id":"W2904624440","doi":"","title":"Evaluating the Loudness Exponent from Auditory Adaptation Data","year":2008,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Noise Effects and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Loudness; Exponent; Adaptation (eye); Speech recognition; Computer science; Psychology; Acoustics; Audiology; Physics; Medicine; Linguistics; Neuroscience","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001203535,0.0001399732,0.0001626207,0.0000391214,0.001495526,0.00001863432,0.000572665,0.00008050301,0.0002597779],"category_scores_gemma":[0.0005230867,0.00009341412,0.00002690195,0.0001409724,0.00007337101,0.0003597426,0.0006294621,0.0003549606,0.000444079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000945094,"about_ca_system_score_gemma":0.0001225841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000546077,"about_ca_topic_score_gemma":0.00003158927,"domain_scores_codex":[0.9983961,0.00007821983,0.0003293471,0.0004046811,0.0004564469,0.0003352081],"domain_scores_gemma":[0.9987428,0.0003470967,0.0002367854,0.0004040827,0.0001982846,0.0000710196],"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":[0.0002217419,0.000160083,0.01066014,0.000473508,0.0001450725,0.00001128014,0.05665835,0.0000647448,0.01848501,0.004157565,0.8788174,0.03014505],"study_design_scores_gemma":[0.003654066,0.0005252436,0.3453691,0.001200298,0.0003162177,0.0000050423,0.06085869,0.09419516,0.0003518099,0.0031643,0.489423,0.0009370013],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860768,0.0002411024,0.0005281875,0.001681397,0.002236642,0.001131286,0.00001948766,0.0001603026,0.007924771],"genre_scores_gemma":[0.9913444,0.00009101538,0.001475992,0.00115964,0.002616647,0.0002770167,0.00008468893,0.00003018779,0.002920431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3893944,"threshold_uncertainty_score":0.9998044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4816814625554889,"score_gpt":0.5020492769485791,"score_spread":0.02036781439309021,"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."}}