{"id":"W4388044263","doi":"10.5539/jsd.v16n6p42","title":"Spatial and Temporal Variability of Vegetation Indices with Industrial Tomato Yield","year":2023,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normalized Difference Vegetation Index; Vegetation (pathology); Environmental science; Enhanced vegetation index; Hectare; Crop; Solanum; Yield (engineering); Spatial variability; Agronomy; Crop yield; Spectroradiometer; Vegetation Index; Leaf area index; Mathematics; Horticulture; Biology; Ecology; Statistics; Reflectivity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002003221,0.00008416489,0.00008880231,0.0003775744,0.00009274877,0.0002338237,0.00008733878,0.00009223703,0.0007854028],"category_scores_gemma":[0.0006865878,0.00007527268,0.0001086711,0.0005533361,0.0001013671,0.0001690956,0.0001667612,0.00009966196,0.0001461411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000171223,"about_ca_system_score_gemma":0.0001066425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005867024,"about_ca_topic_score_gemma":0.01296495,"domain_scores_codex":[0.9998841,0.00001860992,0.000009351419,0.00004788628,0.00002320196,0.00001679774],"domain_scores_gemma":[0.9995952,0.0001317382,0.0001388693,0.00002928983,0.00006803942,0.00003685808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005717199,0.00001987384,0.9867364,0.00000874773,0.0000237176,0.0001090182,0.0001992441,0.0004588281,0.007038582,0.00003270648,0.00007213432,0.005243701],"study_design_scores_gemma":[3.322325e-7,0.000008738329,0.99919,9.64515e-7,0.000002682813,0.00002912071,0.00008033862,0.0004707212,0.000127398,0.000007235008,0.00008153159,9.748388e-7],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991372,0.00004228289,0.0002244517,0.000007388081,8.045405e-7,0.000001660527,0.0001364703,0.000005551823,0.0004441708],"genre_scores_gemma":[0.9994107,0.0000244579,0.0001514937,0.000001807965,0.00000133841,0.000002704053,0.0001751433,0.000002018947,0.0002302632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005867024,"threshold_uncertainty_score":0.01166576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01179583626581228,"score_gpt":0.2059355592079277,"score_spread":0.1941397229421154,"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."}}