{"id":"W4415816438","doi":"10.1093/genetics/iyaf237","title":"Xenbase: 25 years of integrating molecular and biomedical data from <i>Xenopus</i>","year":2025,"lang":"en","type":"article","venue":"Genetics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institutes of Health","keywords":"Xenopus; Suite; Variety (cybernetics); Focus (optics); Translation (biology); Exome; Model organism; Genome; Software","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":[0.005149459,0.00192932,0.001715939,0.009999065,0.001237484,0.005848763,0.003695078,0.001835218,0.03098166],"category_scores_gemma":[0.01642349,0.001274799,0.00171213,0.01087726,0.001112933,0.00553258,0.006244508,0.002289497,0.02923576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002219238,"about_ca_system_score_gemma":0.005276827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01001612,"about_ca_topic_score_gemma":0.00978073,"domain_scores_codex":[0.9969919,0.0004899013,0.000763458,0.0005535143,0.001022553,0.0001786309],"domain_scores_gemma":[0.9916157,0.002259076,0.0009984446,0.002583623,0.001677115,0.0008660221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001386309,0.0001361067,0.005679577,0.007164112,0.0007039092,0.0009503296,0.0007283487,0.003249967,0.01846107,0.01963908,0.7771296,0.1647716],"study_design_scores_gemma":[0.00008511171,0.00004881713,0.003414234,0.0009100115,0.0001955197,0.0005170606,0.0001427249,0.001396205,0.008443347,0.006829589,0.9779107,0.0001066779],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.006001828,0.009328503,0.09052444,0.003591401,0.001130772,0.000483582,0.7691273,0.09063311,0.0291791],"genre_scores_gemma":[0.009306849,0.004946622,0.06022401,0.001189616,0.0001464256,0.0004207689,0.9107963,0.009124506,0.003844824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03098166,"threshold_uncertainty_score":0.103644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782972594310606,"score_gpt":0.2962917878350094,"score_spread":0.2784620618919033,"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."}}