{"id":"W2895784698","doi":"10.1101/439505","title":"Crop Information Engine and Research Assistant (CIERA) for managing genealogy, phenotypic and genotypic data for breeding programs","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"","keywords":"Phenomics; Germplasm; Computer science; Data management; Database; Data science; World Wide Web; Biology; Genomics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002210391,0.0003100632,0.0003359296,0.00008948643,0.0006554587,0.0008499463,0.0006542653,0.0003587016,0.000006792914],"category_scores_gemma":[0.0002393277,0.0001771987,0.00004694121,0.0002853604,0.000160359,0.000268348,0.001093912,0.0003278539,0.000002976008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005100396,"about_ca_system_score_gemma":0.0000616121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001084667,"about_ca_topic_score_gemma":0.00004046448,"domain_scores_codex":[0.9978253,0.00005638322,0.0003981333,0.0007801511,0.0002857521,0.0006543127],"domain_scores_gemma":[0.998363,0.0002393873,0.0002217629,0.0003264606,0.0006358695,0.0002135197],"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":[0.0002756256,0.0001130904,0.01303017,0.002169717,0.000258676,0.00000406263,0.00006076517,0.00001642032,0.971778,0.002028444,0.001924931,0.008340095],"study_design_scores_gemma":[0.002101027,0.002307722,0.4523408,0.001716213,0.0004804014,4.245409e-7,0.0001258639,0.08474962,0.03088645,0.0004602017,0.4215803,0.003251028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916096,0.001685908,0.001550055,0.0006520214,0.0005068526,0.001939413,0.001930474,0.0001189711,0.000006715511],"genre_scores_gemma":[0.9831515,0.0005851212,0.0147931,0.00006912358,0.001088267,0.0002502245,0.00005028625,0.000009111338,0.000003265684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9408916,"threshold_uncertainty_score":0.8196052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08288805094533813,"score_gpt":0.2665094750357941,"score_spread":0.183621424090456,"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."}}