{"id":"W2069043569","doi":"10.2134/agronj2002.3370","title":"Timothy Yield and Nutritive Value by the CATIMO Model","year":2002,"lang":"en","type":"article","venue":"Agronomy Journal","topic":"Crop Yield and Soil Fertility","field":"Agricultural and Biological Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Forage; Dry matter; Leaf area index; Interception; Yield (engineering); Limiting; Agronomy; Growth model; Dynamic simulation model; Mathematics; Model validation; Calibration; Environmental science; Animal science; Biology; Statistics; Ecology; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002988976,0.000781778,0.0003649265,0.0003097275,0.0004617447,0.001006141,0.001492833,0.0006700209,0.002997301],"category_scores_gemma":[0.0009006105,0.0002530533,0.0006191455,0.0005366591,0.0003335906,0.0006135883,0.0004461637,0.0004185433,0.0003680329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003784915,"about_ca_system_score_gemma":0.002717338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3920959,"about_ca_topic_score_gemma":0.2742887,"domain_scores_codex":[0.9998679,0.00002276858,0.000004347019,0.00003708635,0.00003150598,0.00003648242],"domain_scores_gemma":[0.9997516,0.00007395363,0.0000324323,0.00001707852,0.0001022835,0.00002261895],"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":[0.00004296595,0.00001417234,0.00252972,0.00001819062,0.00001817295,0.00002509927,0.00003102754,0.9897822,0.001051724,0.002763722,0.0007447816,0.002978142],"study_design_scores_gemma":[0.00001080503,0.00001232551,0.0009798356,0.000003484356,0.00001537822,0.0000104591,0.00001320776,0.9965928,0.0002634687,0.0005999425,0.001489339,0.00000892784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8099977,0.000521231,0.1128001,0.0006134213,0.00005654495,0.0001510548,0.006140443,0.001176426,0.06854296],"genre_scores_gemma":[0.9796723,0.0002538506,0.009608656,0.00006276745,0.000009600031,0.0001089035,0.00145187,0.0001111671,0.008720867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3920959,"threshold_uncertainty_score":0.7796272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02892931831698998,"score_gpt":0.1950198906403777,"score_spread":0.1660905723233878,"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."}}