{"id":"W1495078667","doi":"10.1111/j.1439-0523.2012.02006.x","title":"Comparison of organic and conventional selection environments for spring wheat","year":2012,"lang":"en","type":"article","venue":"Plant Breeding","topic":"Agronomic Practices and Intercropping Systems","field":"Agricultural and Biological Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Agriculture and Agri-Food Canada","keywords":"Organic farming; Biology; Selection (genetic algorithm); Agriculture; Organic production; Cultivar; Yield (engineering); Agronomy; Ecology; Computer science; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001806777,0.0001788316,0.0001167599,0.0002750307,0.0002778272,0.0002838866,0.0002237231,0.00007868429,0.00283184],"category_scores_gemma":[0.0003872545,0.00007671014,0.0001214802,0.0002860618,0.0002131947,0.0001334539,0.0004169056,0.0001242627,0.0002309863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002964013,"about_ca_system_score_gemma":0.0003347238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001986231,"about_ca_topic_score_gemma":0.01460291,"domain_scores_codex":[0.9997525,0.00005096456,0.00001646001,0.00007713582,0.00005044619,0.00005253879],"domain_scores_gemma":[0.9995284,0.00009724146,0.0001337925,0.00005382053,0.00006506834,0.0001216984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005203812,0.001263737,0.266117,0.0001800688,0.0001459647,0.0007842171,0.0008478535,0.0006432224,0.6399835,0.0007032366,0.001119914,0.08300759],"study_design_scores_gemma":[0.0001363369,0.003611366,0.9657902,0.00001118634,0.00008668835,0.0003113831,0.00106975,0.0005881878,0.02327619,0.000166554,0.004928057,0.00002408744],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998765,0.00001197576,0.000298544,0.000009472962,0.000004948147,0.00001309709,0.0001065984,0.000007145611,0.0007832124],"genre_scores_gemma":[0.995637,0.00003592677,0.002077486,0.00003232594,0.000005654158,0.00004976136,0.0006935856,0.00001000275,0.001458235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00283184,"threshold_uncertainty_score":0.009473443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06650439645085084,"score_gpt":0.2600718792397805,"score_spread":0.1935674827889297,"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."}}