{"id":"W4303945912","doi":"10.3390/land11101752","title":"Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data","year":2022,"lang":"en","type":"article","venue":"Land","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung; Norges Forskningsråd; Biodiversa+; National Science Foundation","keywords":"Yield (engineering); Agriculture; Mean squared error; Mathematics; Crop yield; Vegetation (pathology); Statistics; Coefficient of determination; Linear regression; Agronomy; Environmental science; Ecology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004725568,0.0003931181,0.0001860013,0.0004857944,0.0001414528,0.0003441633,0.0002758171,0.0002197052,0.0004781327],"category_scores_gemma":[0.001402824,0.0001204558,0.0003307434,0.0007555261,0.0001359256,0.0004271733,0.0002731682,0.0001340689,0.0001544912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005009446,"about_ca_system_score_gemma":0.0002625973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03871299,"about_ca_topic_score_gemma":0.05602357,"domain_scores_codex":[0.999837,0.00004772396,0.00001671743,0.00004611197,0.00003035148,0.00002204959],"domain_scores_gemma":[0.9994094,0.0002107899,0.0001645191,0.00007170998,0.0001120367,0.00003157489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001035439,0.00004969787,0.934252,0.00004204173,0.00009028831,0.0002301908,0.0001746858,0.04485242,0.005914195,0.00007901506,0.0001372595,0.01407469],"study_design_scores_gemma":[0.000009332605,0.00009466405,0.868635,0.00001605609,0.00003716535,0.00005126705,0.0002564025,0.1273597,0.00304167,0.00006909416,0.000416155,0.00001352115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983894,0.00002979072,0.001015036,0.000007503388,6.798656e-7,0.000006935463,0.0004066509,0.0000223603,0.0001217604],"genre_scores_gemma":[0.9967515,0.00004223998,0.002110029,0.000003479531,0.000001182854,0.00001000364,0.0009478687,0.00000392675,0.0001297689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03871299,"threshold_uncertainty_score":0.07697529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04654020668303321,"score_gpt":0.2362651588469597,"score_spread":0.1897249521639265,"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."}}