{"id":"W2562077745","doi":"10.1016/j.cpb.2016.12.005","title":"Gramene database: Navigating plant comparative genomics resources","year":2016,"lang":"en","type":"article","venue":"Current Plant Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; Guangxi Academy of Sciences","keywords":"Genomics; Annotation; Comparative genomics; Biology; Genome; Exploit; Computational biology; Resource (disambiguation); Phylogenetic tree; Biological database; Data science; Computer science; Bioinformatics; Genetics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001529764,0.0002193617,0.0002498764,0.00002569268,0.0001288596,0.00001239498,0.0002644621,0.0001026224,0.00001236212],"category_scores_gemma":[0.00003566033,0.0001456513,0.00006736063,0.00003816031,0.0002006583,9.369497e-7,0.0002892319,0.0001004963,0.00002746888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001747465,"about_ca_system_score_gemma":0.00003922449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001409224,"about_ca_topic_score_gemma":0.00004475958,"domain_scores_codex":[0.9987506,0.0000892983,0.0002664096,0.0004733646,0.00005329661,0.0003670311],"domain_scores_gemma":[0.9993541,0.00006752175,0.0001392883,0.0003086775,0.00004277617,0.00008761363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009247791,0.00004316139,0.01036244,0.00001097528,0.00008755569,0.000001544294,0.0001516777,0.00000297871,0.9776257,0.00042168,0.002333316,0.008866489],"study_design_scores_gemma":[0.000869234,0.0003683115,0.003519306,0.00009011521,0.00002921705,0.00004771253,0.0001320002,0.00002625757,0.1958512,0.0002912385,0.7983361,0.0004392487],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860268,0.008824085,0.0005747746,0.0001487574,0.0006360403,0.0001661793,0.00341909,0.000008475602,0.0001957569],"genre_scores_gemma":[0.9940886,0.003654596,0.0004168015,0.00007929517,0.0004936465,0.00002976168,0.001185061,0.00001314613,0.0000390911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7960029,"threshold_uncertainty_score":0.5939489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0477484651633392,"score_gpt":0.3047037742556492,"score_spread":0.25695530909231,"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."}}