{"id":"W1835784777","doi":"10.1109/lman.1993.665362","title":"Achieving METARING performance without insertion buffers","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Insertion loss; Optoelectronics; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001787112,0.001463283,0.001015389,0.0009778389,0.001096848,0.00355599,0.002755598,0.001264279,0.01603702],"category_scores_gemma":[0.005602346,0.0005973672,0.0004665569,0.00177266,0.0007023725,0.007566517,0.002788054,0.00150452,0.006815374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000796725,"about_ca_system_score_gemma":0.001605566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000985781,"about_ca_topic_score_gemma":0.001355808,"domain_scores_codex":[0.9976412,0.0002791474,0.0001836157,0.0002585518,0.0008892445,0.0007482679],"domain_scores_gemma":[0.9943909,0.001233783,0.0003358822,0.002869361,0.0009338697,0.0002362941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003274539,0.0007242859,0.005203904,0.0006553963,0.000118965,0.000450798,0.0006126714,0.01924216,0.4482445,0.05253572,0.02161551,0.4473214],"study_design_scores_gemma":[0.0001975518,0.001249728,0.001301973,0.00006525498,0.0001452588,0.0005521267,0.0004217128,0.2077832,0.7383212,0.02169251,0.02817878,0.00009068521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3740536,0.002904393,0.5107167,0.001412753,0.000536836,0.0002317977,0.0005980995,0.05349221,0.0560535],"genre_scores_gemma":[0.8435685,0.0007134432,0.1349723,0.0006343368,0.0003071423,0.00008787523,0.0007593596,0.001612648,0.01734445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01603702,"threshold_uncertainty_score":0.05364919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837839677714667,"score_gpt":0.2437121855110594,"score_spread":0.2253337887339127,"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."}}