{"id":"W2950735567","doi":"10.22215/etd/2010-09345","title":"Improved methods for generating quasi-gray codes","year":2010,"lang":"en","type":"preprint","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Gray (unit); Computer science; Medicine","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":[0.0008302701,0.0006078198,0.0004706624,0.001735164,0.0004083402,0.0009212857,0.001018845,0.000696669,0.01091389],"category_scores_gemma":[0.003943417,0.0003590894,0.0007235736,0.0008773693,0.0008170592,0.001069597,0.001516228,0.0008940729,0.003372009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005861279,"about_ca_system_score_gemma":0.0007486003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001067466,"about_ca_topic_score_gemma":0.002549787,"domain_scores_codex":[0.9990733,0.0002313139,0.00005945138,0.0001200654,0.0004528898,0.00006295789],"domain_scores_gemma":[0.998512,0.0006235788,0.0000716989,0.0004467208,0.0003028897,0.00004313748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002255397,0.0001071003,0.0006962572,0.0003418117,0.00005659961,0.0001522415,0.0002978003,0.1090558,0.03059746,0.2565655,0.00785718,0.5940467],"study_design_scores_gemma":[0.0001006502,0.0001678397,0.0004963884,0.0000718559,0.00004034835,0.000387777,0.00006310445,0.8068599,0.02996568,0.1383052,0.02349191,0.00004929479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005792415,0.0001598387,0.9895402,0.00007750776,0.00009696752,0.00005789897,0.00008174677,0.0005168182,0.003676601],"genre_scores_gemma":[0.1006324,0.0003115178,0.8836253,0.0001285407,0.00008465305,0.0002080439,0.0003732806,0.0005240978,0.01411222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01091389,"threshold_uncertainty_score":0.03651059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03938209058954817,"score_gpt":0.3694343456734885,"score_spread":0.3300522550839403,"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."}}