{"id":"W4416443308","doi":"10.5376/be.2025.15.0025","title":"Big Data Analytics in Enhancing Maize Breeding Programs","year":2025,"lang":"","type":"article","venue":"Biological Evidence","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Strong","keywords":"Big data; Process (computing); Food security; Work (physics); Analytics; Yield (engineering)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002015603,0.0005342598,0.0007880144,0.00006373389,0.000386125,0.0004409312,0.002779965,0.0007081864,0.0002512533],"category_scores_gemma":[0.002795865,0.0001954373,0.0002121519,0.003334378,0.0004401159,0.0005040255,0.002304044,0.0007033525,0.0001246782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297828,"about_ca_system_score_gemma":0.0000878072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001010039,"about_ca_topic_score_gemma":0.005315148,"domain_scores_codex":[0.9951921,0.0004039689,0.001097711,0.001780133,0.0003968517,0.001129232],"domain_scores_gemma":[0.9971337,0.001736958,0.0002641678,0.0004727109,0.0001615899,0.0002308896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001273956,0.0004985346,0.4075776,0.00007829614,0.00005288977,0.00006429388,0.00005421126,0.000005239856,0.102528,0.0008635382,0.003553001,0.4845969],"study_design_scores_gemma":[0.0004195973,0.001604574,0.8949142,0.005526335,0.0001273963,0.00001737154,0.001671643,0.0008186315,0.001812787,0.001914631,0.0899879,0.001184923],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635086,0.01593004,0.0001299423,0.01497806,0.002393786,0.001130983,0.00005683389,0.0001600187,0.001711759],"genre_scores_gemma":[0.9898438,0.005569975,0.0002188798,0.001311539,0.001671237,0.00003400517,0.0001595821,0.000001564888,0.001189444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4873366,"threshold_uncertainty_score":0.7969702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3077370491425819,"score_gpt":0.3230408480316979,"score_spread":0.01530379888911598,"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."}}