{"id":"W4413256580","doi":"10.1080/07373937.2025.2544254","title":"Artificial intelligence application for optimal solar dryer design for agricultural produce","year":2025,"lang":"en","type":"article","venue":"Drying Technology","topic":"Textile materials and evaluations","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Solar dryer; Process engineering; Environmental science; Agriculture; Solar energy; Agricultural machinery; Agricultural engineering; Engineering; Waste management; Computer science; Environmental engineering; Electrical 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":[],"consensus_categories":[],"category_scores_codex":[0.0006296263,0.0001191867,0.0001807144,0.0001448553,0.0003228931,0.000070089,0.0003487543,0.000172298,0.00002738986],"category_scores_gemma":[0.0004936507,0.0001019803,0.00004450763,0.0002937447,0.0001036403,0.00008719952,0.00008049454,0.00005228981,0.00005909252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005353934,"about_ca_system_score_gemma":0.00007219786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000454497,"about_ca_topic_score_gemma":0.00000479964,"domain_scores_codex":[0.9988785,0.00002572092,0.0003176837,0.0004098726,0.00006943131,0.0002987677],"domain_scores_gemma":[0.9992429,0.0001271944,0.0001046656,0.00029562,0.00021247,0.00001712214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000410826,0.00003725792,0.000003933749,0.00003190229,0.000005366237,5.668471e-8,0.00005122295,0.001120106,0.8797797,0.09241719,0.000460254,0.02605191],"study_design_scores_gemma":[0.00005673869,0.00007721352,0.00002390061,0.00001520569,0.00002904901,0.000001482598,0.0001678688,0.005150004,0.8980154,0.09403068,0.002320099,0.0001123054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2510343,0.00003885153,0.7432032,0.003474229,0.0004314099,0.00145628,0.00002831135,0.0003133198,0.00002010953],"genre_scores_gemma":[0.8636853,0.000002397391,0.1333786,0.0000516213,0.0001027238,0.002635202,0.00002345983,0.00001004633,0.000110656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6126509,"threshold_uncertainty_score":0.4158635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04837099209279472,"score_gpt":0.3243192320420704,"score_spread":0.2759482399492756,"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."}}