{"id":"W4304775501","doi":"10.3390/plants11202691","title":"UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies","year":2022,"lang":"en","type":"article","venue":"Plants","topic":"Crop Yield and Soil Fertility","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"Saskatchewan Pulse Growers; Canada First Research Excellence Fund; University of Saskatchewan","keywords":"Growing season; Biomass (ecology); Crop; Vegetation (pathology); Environmental science; Biomass partitioning; Volume (thermodynamics); Standing crop; Remote sensing; Agronomy; Biology; Geography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001387009,0.00008984416,0.000238818,0.00003581312,0.0002416869,0.00002425211,0.0002222982,0.00003024487,0.004319204],"category_scores_gemma":[0.00003922023,0.00003761302,0.0001201801,0.0006175324,0.0000746341,0.00003836214,0.00007412093,0.00009469382,0.000005071158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002053336,"about_ca_system_score_gemma":0.00001483725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003604643,"about_ca_topic_score_gemma":0.002863121,"domain_scores_codex":[0.9990997,0.00008839169,0.0002026246,0.0002129975,0.0002153246,0.0001809722],"domain_scores_gemma":[0.9995848,0.0001630955,0.00008779483,0.00006090725,0.00005069298,0.00005269526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002511136,0.0002469243,0.7502161,0.00001509508,0.000307947,0.00001725364,0.0002142086,0.0001234673,0.2277277,0.00005372897,0.001157248,0.01966923],"study_design_scores_gemma":[0.0001202633,0.00008154951,0.9907351,0.000002662487,0.0001037715,0.00000217078,0.0002337958,0.001053983,0.006166929,0.00006817762,0.001299868,0.0001316863],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974236,0.00005181302,0.000001289127,0.0002853561,0.0001210281,0.00006145741,0.00107524,0.00003690161,0.0009432668],"genre_scores_gemma":[0.9993698,0.000005067049,0.00004762522,0.0001292864,0.00003141062,0.000008240922,0.0002914742,4.256828e-7,0.0001166663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2405191,"threshold_uncertainty_score":0.996591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170083160793281,"score_gpt":0.2106287041063083,"score_spread":0.1936203880269802,"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."}}