{"id":"W4413081031","doi":"10.2139/ssrn.5355177","title":"Optimizing Agrivoltaic Shading for Climate-Resilient Crop Production: Amaranth Performance Under Current and Future Climatic Scenarios","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Photovoltaic Systems and Sustainability","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Amaranth; Shading; Production (economics); Environmental science; Crop; Crop production; Agricultural engineering; Climate change; Agroforestry; Current (fluid); Agronomy; Biology; Computer science; Agriculture; Economics; Ecology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003502664,0.0005020504,0.0005874571,0.000138295,0.001012279,0.0002021553,0.0005178294,0.0002575389,0.00005818138],"category_scores_gemma":[0.00007192555,0.000424346,0.0002592867,0.0002265121,0.0001835988,0.0002978219,0.0007656736,0.003153565,0.000008673718],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005212926,"about_ca_system_score_gemma":0.001219939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002193244,"about_ca_topic_score_gemma":0.0007962346,"domain_scores_codex":[0.9948813,0.0001547962,0.0007880547,0.0009252813,0.0004939508,0.002756624],"domain_scores_gemma":[0.9986681,0.00005388856,0.0005059292,0.0005221951,0.00008525154,0.0001646054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001713304,0.001704287,0.2422552,0.01851138,0.001348147,0.00001253072,0.009034783,0.2532339,0.002454454,0.008960834,0.005589046,0.4551822],"study_design_scores_gemma":[0.01218571,0.003884253,0.1552304,0.01053203,0.003099314,0.003592765,0.04851748,0.2352347,0.001919784,0.3094349,0.2054632,0.01090545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9769221,0.01184063,0.004135998,0.00117244,0.003149866,0.002285883,0.00002198922,0.00007322572,0.0003979008],"genre_scores_gemma":[0.973754,0.02315237,0.0006360866,0.00005614214,0.001265276,0.0002293882,0.00001647455,0.00003947093,0.0008508046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4442767,"threshold_uncertainty_score":0.9998208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009456314121590328,"score_gpt":0.2541938451763817,"score_spread":0.2447375310547913,"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."}}