{"id":"W4389880933","doi":"10.1007/s10470-023-02198-0","title":"Hard-disk drive read-channel design trade-offs for areal densities beyond 2 Tb/in2","year":2023,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Works and Government Services Canada","funders":"","keywords":"Computer science; Power consumption; Patterned media; Offset (computer science); Channel (broadcasting); Computer hardware; Area density; Electronic engineering; Real-time computing; Electrical engineering; Power (physics); Engineering; Telecommunications; Head (geology)","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"],"consensus_categories":[],"category_scores_codex":[0.000489989,0.0003711517,0.0004295801,0.0004502488,0.0006476315,0.0004996277,0.0008169563,0.0002262369,0.000004488329],"category_scores_gemma":[0.0001747432,0.0003128573,0.00008202595,0.00127389,0.0002863991,0.001329005,0.0001637094,0.0003722341,0.00001074421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007859265,"about_ca_system_score_gemma":0.0002167973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003167749,"about_ca_topic_score_gemma":0.00001497015,"domain_scores_codex":[0.9976879,0.0000665273,0.0004049207,0.0008463442,0.0003021989,0.0006920514],"domain_scores_gemma":[0.9987129,0.0003491605,0.0002082052,0.0003658838,0.0002359279,0.0001279351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002753701,0.00005411852,0.00006691275,0.0001603249,0.00009241245,0.0001951314,0.003073829,0.002128394,0.01392189,0.01940996,0.006343704,0.9545258],"study_design_scores_gemma":[0.0008871316,0.0006460396,0.0005865198,0.0004275989,0.00007822794,0.0001452017,0.005505845,0.7178842,0.01533473,0.2558988,0.001545354,0.001060283],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003955162,0.001041623,0.9920601,0.0006961349,0.0001165957,0.0003691661,0.00007818199,0.001420274,0.0002627146],"genre_scores_gemma":[0.9809289,0.0001588094,0.01778465,0.0004700545,0.00007960548,0.0001111581,0.0001185029,0.00004141295,0.0003069391],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9769737,"threshold_uncertainty_score":0.9999323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04407113161924989,"score_gpt":0.2632933852471884,"score_spread":0.2192222536279385,"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."}}