{"id":"W4392201564","doi":"10.3390/agronomy14030477","title":"Advancing toward Personalized and Precise Phosphorus Prescription Models for Soybean (Glycine max (L.) Merr.) through Machine Learning","year":2024,"lang":"en","type":"article","venue":"Agronomy","topic":"Plant nutrient uptake and metabolism","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Glycine; Phosphorus; Medical prescription; Artificial intelligence; Computer science; Machine learning; Chemistry; Biology; Biochemistry; Pharmacology; Amino acid","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005433694,0.0006075392,0.0003087343,0.0002996889,0.000163925,0.000512017,0.0004107446,0.000501777,0.0003884466],"category_scores_gemma":[0.0009026285,0.000229026,0.0004070629,0.0001944299,0.000130952,0.0004281403,0.0002197282,0.0004411585,0.0001522797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005399737,"about_ca_system_score_gemma":0.0006988738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01055158,"about_ca_topic_score_gemma":0.01106465,"domain_scores_codex":[0.9998895,0.00002911176,0.00000677031,0.00004513598,0.00001754145,0.00001193947],"domain_scores_gemma":[0.999726,0.0001534063,0.00005141619,0.00001447056,0.000044854,0.000009876071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006505202,0.0001009014,0.006480839,0.00004053631,0.00005068938,0.00003298182,0.00002962568,0.9541552,0.005197193,0.0003199229,0.0002754827,0.03325155],"study_design_scores_gemma":[0.000002877365,0.00003188525,0.001076796,0.000003441893,0.00000856129,0.00000598276,0.000006671851,0.997454,0.0009624477,0.0003233359,0.0001195978,0.000004356217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5907016,0.000487114,0.4039581,0.0004044185,0.0000257031,0.00008632969,0.0004648172,0.001207843,0.002664203],"genre_scores_gemma":[0.9584718,0.0001133662,0.04027058,0.00007141434,0.000006206677,0.00005889236,0.000275572,0.00001907202,0.0007129886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01055158,"threshold_uncertainty_score":0.0209803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02713760647699156,"score_gpt":0.2331594125971536,"score_spread":0.206021806120162,"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."}}