{"id":"W2264423257","doi":"10.1159/000430427","title":"Database and Informatic Challenges in Representing Both Diploid and Tetraploid &lt;b&gt;&lt;i&gt;Xenopus&lt;/i&gt;&lt;/b&gt; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02825757,0.0008458606,0.00191809,0.006049336,0.002699711,0.02074977,0.006125855,0.003554074,0.007874978],"category_scores_gemma":[0.05751688,0.001527837,0.002108793,0.0152688,0.003304019,0.02877217,0.008782616,0.007146713,0.006751692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003776995,"about_ca_system_score_gemma":0.005437483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009885216,"about_ca_topic_score_gemma":0.007867839,"domain_scores_codex":[0.9832289,0.006341086,0.003296675,0.001941336,0.004724525,0.0004675222],"domain_scores_gemma":[0.9417453,0.01853064,0.002622435,0.02505469,0.01071018,0.001336748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003163067,0.0001573522,0.003667045,0.001316985,0.0002442157,0.00121659,0.00366844,0.008089505,0.004308371,0.5461724,0.1395114,0.2913314],"study_design_scores_gemma":[0.00006815724,0.00004771329,0.0007354324,0.0007891186,0.0001190708,0.0008923131,0.002294964,0.01314093,0.003166957,0.2074878,0.7711697,0.00008785557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01308229,0.01183862,0.8091339,0.07691251,0.003277683,0.0006198648,0.02614927,0.01372661,0.04525933],"genre_scores_gemma":[0.07041399,0.007872331,0.8503112,0.0141123,0.0008897625,0.0005520635,0.04009581,0.002775363,0.01297716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02825757,"threshold_uncertainty_score":0.1494421,"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."}}