{"id":"W2066796066","doi":"10.1109/gensips.2009.5174349","title":"Subspace pursuit for gene profile classificaiton","year":2009,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subspace topology; Sparse approximation; Computer science; Representation (politics); Artificial intelligence; Pattern recognition (psychology); Selection (genetic algorithm); Support vector machine; Genetic programming; Random subspace method; Machine learning; Gene selection; Gene; Biology; Genetics","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":[0.00004107691,0.00006163749,0.00005556528,0.00002218428,0.00004386997,0.00004006246,0.0001022636,0.00003865078,0.00003346623],"category_scores_gemma":[0.00000403482,0.00005689921,0.00002578188,0.00007723789,0.000007107087,0.00005430074,0.000003968086,0.00004030206,0.0000193879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001721401,"about_ca_system_score_gemma":0.000005311229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.227375e-7,"about_ca_topic_score_gemma":4.586712e-7,"domain_scores_codex":[0.9996681,9.525607e-7,0.00007319306,0.0000846318,0.00004281009,0.0001302697],"domain_scores_gemma":[0.9998021,0.000007154089,0.000008150671,0.0001287152,0.00002623597,0.00002761262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004120915,0.00004688608,0.000008388043,0.00004339448,0.000006657813,4.283947e-7,0.00005016473,0.0001632246,0.6043391,0.02128066,0.215534,0.1585231],"study_design_scores_gemma":[0.0001207085,0.00003858081,0.0002307404,0.000007150598,0.000007666552,0.000002876193,0.00001141401,0.08462087,0.7797313,0.006789464,0.1282668,0.0001724486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002616084,0.0001194248,0.9602985,0.00133377,0.00003193037,0.0003214699,0.000007561752,0.001569233,0.03370205],"genre_scores_gemma":[0.6021401,0.00001966247,0.393886,0.0001670667,0.00009470907,0.0001824887,0.00002099453,0.00001879465,0.003470168],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.599524,"threshold_uncertainty_score":0.2320282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686810256034458,"score_gpt":0.2601452832149622,"score_spread":0.2432771806546176,"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."}}