{"id":"W4413321422","doi":"10.23919/oecc/psc62146.2025.11109328","title":"Algorithm Finding GMI-Optimal Binary Labelings for BICM","year":2025,"lang":"en","type":"article","venue":"","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Infineon Technologies (Canada)","funders":"Engineering and Physical Sciences Research Council; University College London; European Commission; Royal Academy of Engineering","keywords":"Binary number; Computer science; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005815363,0.0007043428,0.0006683147,0.001078409,0.0008612908,0.001012498,0.0008860299,0.0007920219,0.01321293],"category_scores_gemma":[0.00230735,0.0004192101,0.0002733141,0.001166307,0.0006068317,0.0007660164,0.001484547,0.0009232975,0.004388872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009467884,"about_ca_system_score_gemma":0.001460365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001439965,"about_ca_topic_score_gemma":0.002748046,"domain_scores_codex":[0.99957,0.0001180023,0.00002019852,0.00007062263,0.0001562375,0.00006490537],"domain_scores_gemma":[0.9994391,0.0002208497,0.00005130246,0.00008608466,0.0001755444,0.00002711609],"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":[0.000469143,0.0001058493,0.0006490791,0.0002894858,0.00003228882,0.0001323366,0.0002857516,0.09792549,0.02804465,0.08620102,0.02321754,0.7626474],"study_design_scores_gemma":[0.0001949622,0.0001586338,0.0005329288,0.0001009259,0.00002869657,0.0003495062,0.0002422547,0.8153101,0.03035668,0.1217991,0.03085717,0.00006905601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009719095,0.0001793494,0.9720598,0.0003178461,0.00004896501,0.0001096514,0.0002849912,0.00219798,0.01508232],"genre_scores_gemma":[0.09216457,0.0001099637,0.9022149,0.0001215298,0.00002229537,0.0002452207,0.0005790025,0.0002362416,0.004306309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01321293,"threshold_uncertainty_score":0.04420161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169344341154476,"score_gpt":0.304735335399959,"score_spread":0.2878009012845114,"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."}}