{"id":"W2761970546","doi":"10.1109/cibcb.2017.8058532","title":"Hybridization and ring optimization for larger sets of embeddable biomarkers","year":2017,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University; University of Guelph","funders":"","keywords":"Computer science; Algorithm; Code (set theory); Metric (unit); Ring (chemistry); Set (abstract data type); Alphabet; Levenshtein distance; Point (geometry); Evolutionary algorithm; Genetic algorithm; Theoretical computer science; Mathematics; Artificial intelligence; Machine learning; Engineering","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":[0.002698211,0.0007734447,0.001064263,0.0007519008,0.0005282842,0.0009531271,0.00140892,0.001360699,0.002901161],"category_scores_gemma":[0.0101375,0.0004667687,0.0008894429,0.000517374,0.001372856,0.002380224,0.001682621,0.001380462,0.0004553958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354135,"about_ca_system_score_gemma":0.0007508858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001111373,"about_ca_topic_score_gemma":0.001370637,"domain_scores_codex":[0.9989368,0.0003546604,0.00004519967,0.0003150874,0.0002289967,0.0001192095],"domain_scores_gemma":[0.9945037,0.003977927,0.0004229457,0.0005665341,0.0003673487,0.000161475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001615714,0.0001299297,0.001222797,0.00009859476,0.00005766738,0.00007784231,0.0001002991,0.8983383,0.01416286,0.03933175,0.0005927873,0.04572555],"study_design_scores_gemma":[0.00001869615,0.0001667486,0.0001662571,0.00000930332,0.00001851164,0.00003307564,0.00002965399,0.9793844,0.008849868,0.01043524,0.0008735706,0.0000147406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2380419,0.0004935145,0.7521959,0.0004288892,0.00007737259,0.00008812823,0.00006687183,0.000599384,0.008008014],"genre_scores_gemma":[0.5807118,0.0001626082,0.4128638,0.0002125553,0.00002978823,0.0001460997,0.0001243228,0.0001869023,0.005562023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002901161,"threshold_uncertainty_score":0.01426971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759192469334472,"score_gpt":0.2765656658025655,"score_spread":0.2589737411092208,"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."}}