{"id":"W4402318539","doi":"10.3390/s24175794","title":"Forage Height and Above-Ground Biomass Estimation by Comparing UAV-Based Multispectral and RGB Imagery","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Beef Cattle Research Council","keywords":"Remote sensing; Multispectral image; RGB color model; Normalized Difference Vegetation Index; Environmental science; Image resolution; Forage; Canopy; Biomass (ecology); Population; Leaf area index; Computer science; Geography; Artificial intelligence; Agronomy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002304641,0.0003113939,0.0001922187,0.0007735695,0.0001000193,0.0003928787,0.0001877903,0.0001921441,0.0004822717],"category_scores_gemma":[0.0004277876,0.0001221164,0.0002549121,0.0005176414,0.00009381583,0.0003616931,0.0001951991,0.00009894116,0.0001713131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002199379,"about_ca_system_score_gemma":0.0001260478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008519902,"about_ca_topic_score_gemma":0.02049311,"domain_scores_codex":[0.9998604,0.00001649113,0.000007457437,0.00003857886,0.00005448389,0.00002247463],"domain_scores_gemma":[0.9998553,0.00003358518,0.00002653555,0.00001915747,0.00005532488,0.00001006999],"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":[0.0007119068,0.0002200422,0.3196275,0.0003462929,0.0004786139,0.0002329994,0.0003927962,0.0520465,0.3800121,0.0004446226,0.0008708285,0.2446159],"study_design_scores_gemma":[0.00002688736,0.000209022,0.752671,0.00002727288,0.0001427938,0.0001870127,0.0004448299,0.1902376,0.05459079,0.000143137,0.001271969,0.00004763116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888938,0.0002007409,0.009291826,0.00001334796,0.000009798593,0.00001129173,0.0003778165,0.0001276004,0.001073826],"genre_scores_gemma":[0.9876408,0.000100376,0.01141635,0.00001236503,0.000003447457,0.000007460304,0.0005267904,0.00001161594,0.0002807565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008519902,"threshold_uncertainty_score":0.01694059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005898268857814111,"score_gpt":0.2142344958094542,"score_spread":0.2083362269516401,"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."}}