{"id":"W1980217168","doi":"10.4141/cjps07048","title":"Functional genomic resources for potato","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Plant Science","topic":"Plant Pathogens and Resistance","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Agriculture and Agri-Food Canada","funders":"","keywords":"Genomics; Biology; Selection (genetic algorithm); Computational biology; DNA microarray; Functional genomics; Genome; Sequence (biology); Function (biology); Biotechnology; Data science; Genetics; Computer science; Gene; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002688921,0.001355509,0.001251486,0.005178654,0.001197918,0.002012738,0.002126462,0.0008995535,0.03903317],"category_scores_gemma":[0.008534678,0.0009261668,0.001384179,0.009560253,0.0004571904,0.001730815,0.001882154,0.001770058,0.02723484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079686,"about_ca_system_score_gemma":0.002289724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004392539,"about_ca_topic_score_gemma":0.005585654,"domain_scores_codex":[0.9992538,0.0001377528,0.0001313261,0.0002087501,0.000205153,0.00006325426],"domain_scores_gemma":[0.9966684,0.001432262,0.000344574,0.000736809,0.000573275,0.0002446582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001919403,0.0002604029,0.005675325,0.009387495,0.0003108846,0.001949314,0.002018382,0.006724672,0.1188815,0.03209609,0.2659466,0.5548298],"study_design_scores_gemma":[0.0002344196,0.0001550541,0.008035819,0.001019367,0.0002056497,0.001317018,0.0002777906,0.00375091,0.02286034,0.0226293,0.9394109,0.0001034208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01416481,0.006725361,0.309951,0.00230014,0.0004458651,0.0009197972,0.5784541,0.044887,0.042152],"genre_scores_gemma":[0.0177354,0.003847777,0.276836,0.0004451446,0.0001389726,0.001050576,0.6874543,0.005141345,0.007350569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03903317,"threshold_uncertainty_score":0.130579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02569469094041613,"score_gpt":0.17384182377546,"score_spread":0.1481471328350438,"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."}}