{"id":"W4385446681","doi":"10.3390/agriculture13081532","title":"Novel Curve Fitting Analysis of NDVI Data to Describe Turf Fertilizer Response","year":2023,"lang":"en","type":"article","venue":"Agriculture","topic":"Turfgrass Adaptation and Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; Agrotain","keywords":"Normalized Difference Vegetation Index; Poa pratensis; Fertilizer; Loam; Mathematics; Environmental science; Agronomy; Lawn; Canopy; Remote sensing; Leaf area index; Soil science; Soil water; Poaceae; Biology; Botany","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004600776,0.00009782407,0.0001461144,0.00009054293,0.00006662636,0.00002576635,0.0004478221,0.00004027146,0.0005387794],"category_scores_gemma":[0.0002058637,0.00006579849,0.00006268546,0.002705767,0.00002255748,0.0001495645,0.0005089991,0.0000582278,0.000385857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004637033,"about_ca_system_score_gemma":0.000003441414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004828244,"about_ca_topic_score_gemma":0.001402754,"domain_scores_codex":[0.9989192,0.00004745857,0.0001844158,0.0003458488,0.0003176669,0.000185401],"domain_scores_gemma":[0.9992957,0.00007561879,0.00006775736,0.0004619746,0.00001412358,0.00008487589],"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":[0.0001472798,0.0001560223,0.0149146,0.00001110021,0.000445592,0.00001420573,0.002786122,0.05900246,0.5611583,0.0002655743,0.3557317,0.005367022],"study_design_scores_gemma":[0.0001088953,0.00001687207,0.9195184,0.000005905808,0.0001663862,3.728684e-7,0.0009877234,0.002565015,0.0005605745,0.000007600282,0.07595345,0.00010878],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918898,0.00001343162,0.001570152,0.003580866,0.00009344666,0.0002632128,0.0001670582,0.0001131682,0.002308814],"genre_scores_gemma":[0.9902645,0.000005432322,0.002482568,0.0005735116,0.00001313736,0.00001178165,0.0002808143,0.000005658382,0.006362577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9046038,"threshold_uncertainty_score":0.5899256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06531586774743173,"score_gpt":0.2844044913678098,"score_spread":0.2190886236203781,"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."}}