{"id":"W3045041747","doi":"10.3390/agronomy10071046","title":"Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms","year":2020,"lang":"en","type":"article","venue":"Agronomy","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":252,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Yield (engineering); Growing season; Support vector machine; Normalized Difference Vegetation Index; Algorithm; Crop yield; Linear regression; Mathematics; Crop; Regression analysis; Precision agriculture; Machine learning; Stepwise regression; Artificial intelligence; Agronomy; Soil science; Agriculture; Environmental science; Computer science; Statistics; Geography; Leaf area index; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004563295,0.0001014888,0.0001065133,0.000001897374,0.0002084326,0.0000656137,0.00005189998,0.00005790186,0.000139566],"category_scores_gemma":[0.00002370077,0.0000354396,0.00003877543,0.0001290662,0.00002810595,0.0001707266,0.00005524597,0.000149417,0.00002997553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005695975,"about_ca_system_score_gemma":0.000002049347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003072337,"about_ca_topic_score_gemma":0.00006058533,"domain_scores_codex":[0.9993938,0.00002561447,0.0001083953,0.0002338427,0.00008107256,0.0001572778],"domain_scores_gemma":[0.9997938,0.00005464588,0.00003984794,0.00001519748,0.00002162209,0.00007492743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005339591,0.00005558134,0.0830862,0.0000201264,0.00005547539,0.00002354569,0.002102338,0.00003241059,0.1985042,0.0001426322,0.01115897,0.7047651],"study_design_scores_gemma":[0.0003623024,0.0008783547,0.2340075,0.00005862694,0.00005453789,0.0000512346,0.001393661,0.004454448,0.01778215,0.0001907556,0.7402552,0.0005112214],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888146,0.0004095613,0.0001936711,0.007300022,0.00006127706,0.0001603779,0.00001208017,0.0001431399,0.002905259],"genre_scores_gemma":[0.9975007,0.00003865726,0.0006156234,0.0007398668,0.0008026206,0.000002757704,0.00007459792,7.501276e-7,0.0002244579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7290962,"threshold_uncertainty_score":0.1603116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03317157095661206,"score_gpt":0.1979497935780684,"score_spread":0.1647782226214564,"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."}}