{"id":"W2264423257","doi":"10.1159/000430427","title":"Database and Informatic Challenges in Representing Both Diploid and Tetraploid <b><i>Xenopus</i></b> Species in Xenbase","year":2015,"lang":"en","type":"article","venue":"Cytogenetic and Genome Research","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","keywords":"Schema (genetic algorithms); Xenopus; Biology; Genome; Ploidy; Computational biology; Genetics; Computer science; Information retrieval; Gene","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002460903,0.0004774844,0.0006116909,0.0005272295,0.0003406929,0.0001703637,0.000443851,0.0002874514,0.00001835769],"category_scores_gemma":[0.0005215131,0.0004616439,0.00007057753,0.000412404,0.0008685121,0.00001481528,0.001619071,0.000343015,0.00001141231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006731877,"about_ca_system_score_gemma":0.0002404251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003449279,"about_ca_topic_score_gemma":0.001025876,"domain_scores_codex":[0.9960076,0.0003614077,0.0007364585,0.001095648,0.0006081101,0.00119081],"domain_scores_gemma":[0.9980077,0.0001871515,0.0001357068,0.0008897833,0.0002432726,0.0005363494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001962265,0.0001461993,0.01182052,0.0003573152,0.0001052804,0.00005454693,0.002096747,0.00005835511,0.9788036,0.0005363047,0.0002407133,0.005584202],"study_design_scores_gemma":[0.009686804,0.002898662,0.5217376,0.0004381899,0.0001246424,0.0003551658,0.005759877,0.0009082799,0.05600568,0.002499958,0.397015,0.002570125],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8315275,0.1629723,0.0000228774,0.0003612326,0.00007134006,0.0006773262,0.000120266,0.000007267171,0.004239873],"genre_scores_gemma":[0.8897859,0.1082751,0.0007067344,0.00004869657,0.0002388038,0.0001099801,0.00008670304,0.00005683701,0.000691161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9227979,"threshold_uncertainty_score":0.9997835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09439803817071069,"score_gpt":0.3145470354918256,"score_spread":0.2201489973211149,"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."}}