{"id":"W4393176845","doi":"10.17159/sajs.2024/16059","title":"Selection, sequencing and progression of content in biology in four diverse jurisdictions","year":2024,"lang":"en","type":"article","venue":"South African Journal of Science","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Biology; Computational biology; Positive selection; Evolutionary biology; Biotechnology; Genetics; Computer science; Gene; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00575276,0.0001495663,0.000263649,0.004695844,0.002123527,0.002393708,0.0006159548,0.0005068475,0.002523795],"category_scores_gemma":[0.02680021,0.0002638663,0.0002071654,0.003645913,0.001823361,0.001169173,0.003579251,0.0006031109,0.0003289871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004999888,"about_ca_system_score_gemma":0.00862915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04030995,"about_ca_topic_score_gemma":0.09565854,"domain_scores_codex":[0.9957802,0.001885305,0.0004036078,0.0004160467,0.001010795,0.0005040051],"domain_scores_gemma":[0.976555,0.009398645,0.003350113,0.001006606,0.006854305,0.002835394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004364884,0.000332092,0.6329124,0.0005751022,0.00003748226,0.0009742889,0.2230529,0.0006150596,0.0182818,0.004006205,0.001130584,0.1176455],"study_design_scores_gemma":[0.00001908433,0.000247851,0.8310485,0.0002225043,0.00003776676,0.0001964964,0.1511259,0.001293001,0.00407448,0.001371142,0.01031198,0.00005126722],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968359,0.00007343234,0.0004404207,0.0001188074,0.000004666177,0.00007107132,0.00008177833,0.00001141004,0.002362568],"genre_scores_gemma":[0.9967902,0.00008567133,0.00156106,0.00004184731,0.000002010491,0.0001078654,0.0001841059,0.00001258424,0.001214517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04030995,"threshold_uncertainty_score":0.08015066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0381372624803066,"score_gpt":0.3219165973402148,"score_spread":0.2837793348599082,"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."}}