{"id":"W4380574586","doi":"10.20944/preprints202306.1013.v1","title":"Agricultural Sciences in the Big Data Era: Genotype and Phenotype Data Standardization, Utilization and Integration","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"New Zealand Institute for Plant and Food Research Limited; National Science Foundation","keywords":"Interoperability; Metadata; Standardization; Raw data; Context (archaeology); Data science; Data integration; Translational research; Data type; Computer science; Genotype; Data curation; Biology; World Wide Web; Database; Biotechnology; Gene; Genetics","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":[0.06690943,0.0008282406,0.002371383,0.008992556,0.001196863,0.01230395,0.004381812,0.002696178,0.001607964],"category_scores_gemma":[0.08519471,0.0007607032,0.002157458,0.01795018,0.004690676,0.01728825,0.008530458,0.006383677,0.001214108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004055226,"about_ca_system_score_gemma":0.01225471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004242657,"about_ca_topic_score_gemma":0.003727912,"domain_scores_codex":[0.9798447,0.008360606,0.002765482,0.003048884,0.005296178,0.0006841687],"domain_scores_gemma":[0.8637756,0.07172196,0.006288731,0.03141244,0.02276083,0.00404045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002698224,0.0001000571,0.01423072,0.01356712,0.000779603,0.0004615679,0.003112678,0.004368079,0.005764257,0.2255141,0.08822614,0.6436058],"study_design_scores_gemma":[0.00003247503,0.0000572039,0.01017053,0.007132035,0.0002388115,0.0003031753,0.002083277,0.002667928,0.002689131,0.2095619,0.7649387,0.0001247939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02719047,0.3525851,0.3878593,0.1606553,0.009772861,0.000798416,0.02474113,0.005873762,0.03052356],"genre_scores_gemma":[0.1127856,0.3053778,0.4717092,0.03302377,0.0077641,0.001764262,0.06161374,0.002449404,0.003512058],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06690943,"threshold_uncertainty_score":0.3538551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4053111688248153,"score_gpt":0.4079962002754815,"score_spread":0.002685031450666175,"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."}}