{"id":"W4393424398","doi":"10.5281/zenodo.7488383","title":"Rice pseudoDB: simulated database of rice genetic variants","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Database; Computer science","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":[0.0009677284,0.001757544,0.0009641048,0.00161034,0.0004734793,0.001103737,0.002993396,0.001692496,0.009527164],"category_scores_gemma":[0.003969901,0.0005120031,0.001174327,0.003116244,0.0004172585,0.0006441664,0.0009331133,0.001352257,0.007931857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001107209,"about_ca_system_score_gemma":0.001743263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01413147,"about_ca_topic_score_gemma":0.0218735,"domain_scores_codex":[0.9992754,0.000152059,0.00006481395,0.0002451162,0.0001922296,0.00007041757],"domain_scores_gemma":[0.9987022,0.0005118865,0.00008505331,0.0003413423,0.0002047754,0.0001547611],"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":[0.001538342,0.0004020694,0.01195979,0.001675222,0.0004909682,0.0003878415,0.00005901198,0.02224524,0.002506508,0.00209524,0.9427068,0.01393303],"study_design_scores_gemma":[0.004230487,0.0004603511,0.03636652,0.0004305525,0.0005112157,0.001821691,0.0002674233,0.09486095,0.009196772,0.009635902,0.8420501,0.0001678958],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007951931,0.0002659869,0.001039085,0.0001790849,0.00004413284,0.00003782889,0.9878226,0.001724259,0.0009351262],"genre_scores_gemma":[0.004675573,0.00005531268,0.001118821,0.00005165848,0.000003245913,0.0000606725,0.9937303,0.00004936582,0.0002549902],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01413147,"threshold_uncertainty_score":0.03187156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.027245741343037,"score_gpt":0.2654397265594101,"score_spread":0.2381939852163731,"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."}}