{"id":"W133997040","doi":"10.1007/978-3-642-40104-6_46","title":"The Greedy Gray Code Algorithm","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Gray code; Computer science; Greedy algorithm; Algorithm; Binary tree; Gray (unit); Binary number; Object (grammar); Binary code; Theoretical computer science; Artificial intelligence; Mathematics; Arithmetic","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","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001020451,0.0006180476,0.0004744873,0.000386885,0.001055276,0.001780719,0.007591217,0.0003227517,0.00004046041],"category_scores_gemma":[0.00006678441,0.0003976552,0.0001523968,0.0005275745,0.001136347,0.00104002,0.003633795,0.001072981,0.0003379567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002097907,"about_ca_system_score_gemma":0.0004530319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007542991,"about_ca_topic_score_gemma":0.00006711125,"domain_scores_codex":[0.9953378,0.00004892023,0.0005876523,0.001669236,0.001428956,0.000927415],"domain_scores_gemma":[0.9955593,0.0007605337,0.0003480459,0.002738732,0.0003363519,0.000256994],"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":[9.933921e-7,0.0000117014,0.000003469521,0.000005657916,0.000006950424,0.00003759472,0.0001196994,0.0009413597,0.0000189523,0.01549007,0.0004471369,0.9829164],"study_design_scores_gemma":[0.0001630541,0.0001018917,0.00006197298,0.0001707018,0.00000439875,0.00007671955,1.096441e-7,0.7469013,0.0002525575,0.1995606,0.05216374,0.0005428887],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000003919438,0.001233657,0.9899263,0.001230727,0.003562279,0.000454957,0.00001650745,0.0002026102,0.003369077],"genre_scores_gemma":[0.00162339,0.000343293,0.9924318,0.001527071,0.001147257,0.00003054643,0.00001387543,0.00005562025,0.002827189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9823735,"threshold_uncertainty_score":0.9998475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01397051192823707,"score_gpt":0.2375394365179329,"score_spread":0.2235689245896958,"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."}}