{"id":"W4387385166","doi":"10.21272/jes.2023.10(2).e1","title":"Optimization of Cdx Transcription Factors Characteristics","year":2023,"lang":"en","type":"article","venue":"Journal of Engineering Sciences","topic":"Animal Genetics and Reproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Transcription factor; Repressor; Transcription (linguistics); TOPSIS; Computational biology; Biology; Gene; Computer science; Mathematics; Genetics; Operations research","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":[],"consensus_categories":[],"category_scores_codex":[0.0002455161,0.00004006857,0.00006552634,0.0000863323,0.00002391403,0.000008919712,0.00007857474,0.00003031063,0.000002794528],"category_scores_gemma":[0.00007695183,0.00003097214,0.00004353523,0.0001764968,0.00003216802,0.000005340843,0.000007936112,0.00002879556,2.539402e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002870027,"about_ca_system_score_gemma":0.00002458357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.352859e-7,"about_ca_topic_score_gemma":8.315126e-8,"domain_scores_codex":[0.9995921,0.000005533943,0.0001618971,0.00006338989,0.0001122417,0.00006486343],"domain_scores_gemma":[0.9997538,0.000003654803,0.000103903,0.00003970564,0.00007735592,0.00002162544],"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":[0.000003978198,0.000005795383,0.001233431,0.000007556652,0.000006394754,2.543864e-7,0.00003257273,0.2848345,0.713681,0.00001723621,0.00003592565,0.000141329],"study_design_scores_gemma":[0.0003021038,0.001787744,0.1860811,0.00008119354,0.00004152509,0.00004360754,0.0003684706,0.06444272,0.7414885,0.00002210763,0.005100993,0.0002399665],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780831,0.0001155189,0.02130092,0.00003979785,0.000417606,0.00001749695,0.000001685982,0.00000328483,0.00002062732],"genre_scores_gemma":[0.9975369,0.0003194511,0.001927748,0.000002233473,0.0001599927,1.723789e-7,0.00000333862,0.000003699025,0.00004644974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2203918,"threshold_uncertainty_score":0.1263007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575591357853504,"score_gpt":0.237104587944879,"score_spread":0.221348674366344,"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."}}