{"id":"W1978718503","doi":"10.1109/tim.2014.2313431","title":"On System-on-Chip Testing Using Hybrid Test Vector Compression","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossless compression; Computer science; Test vector; Embedded system; System on a chip; Computer hardware; Test compression; Overhead (engineering); Benchmark (surveying); Data compression; Very-large-scale integration; Automatic test pattern generation; Integration testing; Fault coverage; Electronic circuit; Software; Engineering; Test set; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004482264,0.0001902933,0.000155929,0.0001794496,0.0006054383,0.0001623293,0.0001678743,0.00003304091,0.000004928881],"category_scores_gemma":[0.00004323803,0.0001749359,0.00004698282,0.0002133599,0.00002618518,0.0002065345,0.000002043997,0.0001739736,0.00002521716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002419802,"about_ca_system_score_gemma":0.00004680136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004660142,"about_ca_topic_score_gemma":0.000007612509,"domain_scores_codex":[0.9983461,0.0001135089,0.0002852949,0.0003964008,0.000630277,0.0002284],"domain_scores_gemma":[0.9991195,0.0002412061,0.0001283086,0.0002599235,0.0001191791,0.0001319296],"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":[0.000009526184,0.0005608826,0.0004464914,0.0001668897,0.00004431632,0.000007165181,0.0003766033,0.029093,0.1153059,0.003419158,0.00006050738,0.8505096],"study_design_scores_gemma":[0.002485998,0.001402849,0.002786025,0.002059068,0.00006104186,0.00009221098,0.0002163096,0.7852291,0.2047155,0.0002504039,0.0001053411,0.000596121],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1255094,0.000005517029,0.8713337,0.00009770109,0.0007386665,0.0002200474,0.00000581007,0.0002251401,0.001864099],"genre_scores_gemma":[0.9972547,0.000001344225,0.002309114,0.0003376507,0.0000504965,0.00002168376,9.121183e-7,0.00001333693,0.00001074337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8717453,"threshold_uncertainty_score":0.7133681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06817084494368852,"score_gpt":0.2532771614171576,"score_spread":0.1851063164734691,"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."}}