{"id":"W4295204172","doi":"10.3390/rs14184464","title":"High-Resolution Flowering Index for Canola Yield Modelling","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Canola; Pixel; Leaf area index; Coefficient of determination; Brassica; Canopy; Mathematics; Remote sensing; Environmental science; Horticulture; Agronomy; Botany; Statistics; Geography; Biology; Computer science; Artificial intelligence","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":[0.0003104681,0.0006391926,0.0002768301,0.0008135266,0.0002063631,0.0004767374,0.0005158327,0.0003377992,0.0007777154],"category_scores_gemma":[0.0008660182,0.0001857138,0.0005930563,0.0005863673,0.00010852,0.0003791324,0.0002113948,0.0003604189,0.000316576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006223551,"about_ca_system_score_gemma":0.0003352884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01557554,"about_ca_topic_score_gemma":0.01484613,"domain_scores_codex":[0.9998999,0.00001653367,0.000005243913,0.00003501857,0.00002842825,0.00001490549],"domain_scores_gemma":[0.9998639,0.00006558492,0.00001835331,0.0000113575,0.00003343444,0.00000732569],"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":[0.0001558787,0.0002052432,0.02540717,0.00009039729,0.0001205084,0.0001649868,0.00009793275,0.830247,0.02711951,0.0009251396,0.0009798305,0.1144865],"study_design_scores_gemma":[0.000002577809,0.00001921418,0.006085756,0.000003310515,0.00001229199,0.00002254912,0.00001134135,0.9915686,0.001717988,0.0001517345,0.000394651,0.000009962722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.600642,0.0007425114,0.3917683,0.0001221041,0.00003620854,0.0001144355,0.0007622698,0.002068653,0.003743562],"genre_scores_gemma":[0.9494513,0.000183706,0.04824772,0.00001973522,0.000008165503,0.0000682173,0.0006430943,0.00006492217,0.001313117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01557554,"threshold_uncertainty_score":0.03096974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670668462943856,"score_gpt":0.2071511818706681,"score_spread":0.1904444972412296,"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."}}