{"id":"W2153169282","doi":"10.1186/1471-2164-7-149","title":"Wheat EST resources for functional genomics of abiotic stress","year":2006,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Plant Molecular Biology Research","field":"Agricultural and Biological Sciences","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan; Concordia University; University of Windsor; Université du Québec à Montréal","funders":"Genome Prairie; Génome Québec; Genome Canada; Canarie","keywords":"Expressed sequence tag; Biology; Abiotic stress; Contig; Functional genomics; cDNA library; DNA microarray; Computational biology; Genomics; Genetics; Cluster analysis; Gene; Genome; Complementary DNA; Gene expression; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001697361,0.00009913648,0.000145868,0.00001455686,0.000116842,0.00002406763,0.0002253047,0.0001162303,0.00003950249],"category_scores_gemma":[0.00002785555,0.0000440413,0.000102069,0.00008482965,0.0000946885,0.00001991712,0.00006957371,0.00006250232,0.00001568447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003847814,"about_ca_system_score_gemma":0.0000311675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002079648,"about_ca_topic_score_gemma":0.003587362,"domain_scores_codex":[0.9991755,0.00004258439,0.0001977293,0.0002287166,0.00009869236,0.0002567918],"domain_scores_gemma":[0.9994945,0.0002612875,0.00007386687,0.00005871714,0.00006672102,0.00004492618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001142293,0.00006539172,0.035963,0.00001519396,0.00001031327,8.010181e-7,0.00001656188,0.0003313869,0.9613131,0.0003909517,0.0006182298,0.001160865],"study_design_scores_gemma":[0.0006663514,0.0005336614,0.7413653,0.00001765443,0.00003936078,0.00002968459,0.0003228631,0.001687295,0.1790348,0.002956941,0.07290097,0.0004452206],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981437,0.0002548237,0.0001140574,0.0001951353,0.00007065929,0.0002530876,0.0005310454,0.00001787722,0.0004196179],"genre_scores_gemma":[0.9975396,0.00001583278,0.001067779,0.0000377095,0.0003627687,0.00001338876,0.0004450877,0.0000015685,0.0005162131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7822783,"threshold_uncertainty_score":0.2001832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02990874279439355,"score_gpt":0.2215470637269748,"score_spread":0.1916383209325812,"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."}}