{"id":"W2804587642","doi":"10.1016/b978-0-08-100596-5.22439-6","title":"Molecular Improvement of Grain: Target Traits for a Changing World","year":2018,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Food security; Productivity; Quality (philosophy); Grain quality; Range (aeronautics); Biotechnology; Biology; Natural resource economics; Geography; Business; Engineering; Agronomy; Ecology; Agriculture; Economics; Economic growth","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":[0.000237887,0.0002587316,0.0003930712,0.00006037172,0.0001040631,0.00001720929,0.0002259653,0.0001902838,0.0005557937],"category_scores_gemma":[0.000004452408,0.0001089082,0.0003170042,0.00002954066,0.0001029521,0.000007862128,0.00009420888,0.0001011671,0.00001180182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001086389,"about_ca_system_score_gemma":0.0000122485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.171137e-7,"about_ca_topic_score_gemma":0.00006952351,"domain_scores_codex":[0.9988129,0.000009184068,0.0003145027,0.0003477542,0.0001517045,0.0003639113],"domain_scores_gemma":[0.9995137,0.00003676191,0.000185984,0.00007167055,0.0001142339,0.0000775791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001575924,0.000008510409,7.697362e-7,0.00003644246,0.00003569427,0.000003735233,0.00007511273,8.778348e-8,0.127707,0.003171817,0.0001706388,0.8687744],"study_design_scores_gemma":[0.0001307401,0.0007321667,0.00001275911,0.0001148143,0.00005502986,0.000002418914,0.00001247052,0.000004216008,0.02107396,0.02862336,0.9489555,0.0002824975],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01295587,0.001024208,0.00000953574,0.000199139,0.0003423024,0.0009343807,0.0003566217,0.00002684515,0.9841511],"genre_scores_gemma":[0.01662055,0.00002511637,0.0004220551,0.0004912753,0.0007893028,0.00006254172,0.0001243303,0.000005888609,0.981459],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9487849,"threshold_uncertainty_score":0.6085551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171683329399709,"score_gpt":0.2193290820437373,"score_spread":0.2021607491037664,"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."}}