{"id":"W4402533403","doi":"10.1093/jas/skae234.228","title":"370 Leveraging remote sensing products to estimate forage productivity in the Canadian Prairies","year":2024,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Forage; Productivity; Environmental science; Remote sensing; Agroforestry; Agronomy; Agricultural engineering; Geography; Biology; Engineering; Economics","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":[0.0009844543,0.0004985451,0.0002578102,0.003298465,0.000822473,0.001248656,0.0007521503,0.0002467638,0.001037596],"category_scores_gemma":[0.002528738,0.0002324901,0.0002864868,0.00367615,0.0003251059,0.000359271,0.000370902,0.0002231009,0.000322148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006188856,"about_ca_system_score_gemma":0.004660601,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9683243,"about_ca_topic_score_gemma":0.9829963,"domain_scores_codex":[0.9995412,0.00003895787,0.00002326241,0.0001086509,0.0002227288,0.00006530341],"domain_scores_gemma":[0.9982103,0.0002290994,0.0001601158,0.00008469979,0.001205834,0.0001099138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001138488,0.00006192853,0.9279357,0.00009284861,0.0002649987,0.00009881883,0.0005333589,0.009063493,0.00653542,0.0002024045,0.001524092,0.053573],"study_design_scores_gemma":[0.000005469468,0.000009571061,0.9841896,0.00001625873,0.00002370919,0.00001896686,0.0003264138,0.01376251,0.0005242061,0.00004506368,0.001061051,0.00001718141],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850199,0.0003376111,0.00254224,0.00005919716,0.000007155916,0.00008713393,0.007552833,0.000113582,0.00428038],"genre_scores_gemma":[0.9881825,0.0001830339,0.007046075,0.00001766605,0.000003188085,0.00003100005,0.003641968,0.00001567569,0.0008789449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0316757,"threshold_uncertainty_score":0.0637244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053428585625525,"score_gpt":0.2801347776495822,"score_spread":0.2596004917933269,"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."}}