{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002725131,0.000115051,0.0001345416,0.00001632759,0.0002144443,0.00007732047,0.0004443685,0.00004919513,0.000257136],"category_scores_gemma":[0.0000332302,0.00008268189,0.00001277091,0.0002638802,0.00002606983,0.0001763433,0.0007949936,0.0002952904,0.00003726177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000172626,"about_ca_system_score_gemma":0.000005795837,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01612458,"about_ca_topic_score_gemma":0.003804996,"domain_scores_codex":[0.9988267,0.00008200225,0.0001697962,0.0003577606,0.0003323063,0.0002314703],"domain_scores_gemma":[0.9994465,0.00004922297,0.00007928219,0.0003783036,0.000002181477,0.00004452629],"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":[0.000006906356,0.00005054203,0.2777993,0.00001823987,0.000007650049,0.0001298752,0.0004303994,0.7003443,0.007739896,0.000003429817,0.01295415,0.0005153418],"study_design_scores_gemma":[0.0004817082,0.00005122344,0.1986154,0.0001074358,0.00002709357,0.0009352661,0.0006905384,0.7766744,0.00003027918,0.00004336077,0.02181951,0.0005238097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947469,0.000114702,0.0001968318,0.0001023533,0.0005611469,0.0002442512,0.00004216837,0.00004135965,0.00395026],"genre_scores_gemma":[0.9930744,0.000001563011,0.0059897,0.0001131609,0.0001172961,0.000001763923,0.0001776091,0.00001040408,0.0005141131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07918394,"threshold_uncertainty_score":0.9904271,"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."}}