{"id":"W4412969681","doi":"10.29333/ejosdr/16625","title":"Climate variability and agricultural productivity: A time-series regression analysis of cassava, yam, and maize yields in Wenchi, Ghana","year":2025,"lang":"","type":"article","venue":"European Journal of Sustainable Development Research","topic":"Agricultural Research and Practices","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Agriculture; Food security; Climate change; Crop; Linear regression; Environmental science; Regression analysis; Productivity; Crop yield; Geography; Mathematics; Agronomy; Agroforestry; Statistics; Forestry; Biology; Ecology; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003698312,0.0002484188,0.0002048625,0.0004200712,0.0001582335,0.000395856,0.0001453292,0.0001895173,0.000595797],"category_scores_gemma":[0.001157975,0.000105513,0.0003443704,0.001274496,0.000166302,0.0003379245,0.0002389929,0.0002971905,0.0001237889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005964669,"about_ca_system_score_gemma":0.0003614272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03588511,"about_ca_topic_score_gemma":0.03384881,"domain_scores_codex":[0.9998686,0.00004588374,0.00001117834,0.00002858885,0.00001373846,0.0000319532],"domain_scores_gemma":[0.9994728,0.0001843914,0.0002135627,0.00002664649,0.00005734236,0.00004533987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000103239,0.00003477734,0.9881705,0.00003433911,0.0001257308,0.0005481123,0.0005194403,0.003892375,0.00135167,0.0001156095,0.0002613784,0.004842716],"study_design_scores_gemma":[0.000003148516,0.00003392475,0.9930254,0.00001134717,0.00003468044,0.0001147329,0.0009597476,0.005061732,0.0001995557,0.00005036334,0.0004998185,0.000005672147],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992895,0.00009974508,0.0001538281,0.00005286454,0.000002572522,0.000002094673,0.0002611621,0.000004366373,0.0001338009],"genre_scores_gemma":[0.9994158,0.000104714,0.0001021965,0.000005418615,0.000002678087,0.000003884012,0.0002633039,0.000002053507,0.0001000659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03588511,"threshold_uncertainty_score":0.07135242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03113591498264021,"score_gpt":0.3075322778522437,"score_spread":0.2763963628696035,"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."}}