{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008209262,0.0006729843,0.0004606131,0.0007016363,0.0001819654,0.0005697933,0.0005788719,0.0004044412,0.0005566128],"category_scores_gemma":[0.002112803,0.0002935435,0.0005361972,0.0008798925,0.0002822423,0.0006831071,0.0005276665,0.0005407319,0.0002455789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005174209,"about_ca_system_score_gemma":0.0004186432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01143943,"about_ca_topic_score_gemma":0.007534587,"domain_scores_codex":[0.9997216,0.0000704429,0.00001500873,0.0001047772,0.00006255118,0.00002555444],"domain_scores_gemma":[0.9995523,0.0002318458,0.00006424118,0.00003326273,0.0001030721,0.00001525702],"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":[0.00005707288,0.0000513475,0.004785995,0.00002627565,0.00003635823,0.00003150517,0.0000263532,0.9040347,0.002064882,0.00103279,0.0003380504,0.08751472],"study_design_scores_gemma":[0.000001117178,0.000005750635,0.0005144182,0.000001270321,0.00000164154,0.000001604859,0.000002306247,0.9988182,0.0002005469,0.0003943367,0.00005663492,0.000002007742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2222513,0.0003404906,0.7734491,0.0002019933,0.00002965686,0.00004661214,0.0002143492,0.001095496,0.002370991],"genre_scores_gemma":[0.904864,0.0001912182,0.09320451,0.0000321361,0.00003087656,0.00004217932,0.0003288913,0.00002696563,0.001279212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01143943,"threshold_uncertainty_score":0.02274567,"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."}}