{"id":"W4413989325","doi":"10.47392/irjash.2025.083","title":"Deep Learning for Cotton Disease Detection Lightweight, Explainable and Field-Ready Solutions","year":2025,"lang":"en","type":"article","venue":"International Research Journal on Advanced Science Hub","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Field (mathematics); Deep learning; Artificial intelligence; Computer science; Mathematics","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":[0.0005889031,0.0009588433,0.000569461,0.0005375771,0.0001785663,0.0007654184,0.001084776,0.0008650351,0.002350936],"category_scores_gemma":[0.001203323,0.0002668157,0.0007512953,0.0004532816,0.0002633784,0.001197379,0.0007850455,0.001564535,0.0008764985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007899224,"about_ca_system_score_gemma":0.0008001076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006130734,"about_ca_topic_score_gemma":0.007306872,"domain_scores_codex":[0.9997966,0.00003958223,0.0000129321,0.00007199272,0.00004275434,0.00003607256],"domain_scores_gemma":[0.999736,0.0001176082,0.00002952886,0.00002836072,0.00007128686,0.00001724198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001562153,0.0001461122,0.00541642,0.0008505198,0.0002542195,0.000198681,0.0001107525,0.2186786,0.01095578,0.01516343,0.02020666,0.7278627],"study_design_scores_gemma":[0.00001273073,0.00006379636,0.001452859,0.00009369717,0.00006811994,0.00007898917,0.0000338375,0.9638268,0.003969318,0.01702498,0.01335346,0.00002128507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03629505,0.02698515,0.920133,0.003350578,0.0003539529,0.00007237222,0.001527322,0.004549125,0.006733431],"genre_scores_gemma":[0.713265,0.02538542,0.2347803,0.001846902,0.0005302433,0.0002508263,0.005502784,0.0003410828,0.01809748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006130734,"threshold_uncertainty_score":0.0121901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04317754693384679,"score_gpt":0.3582951670801626,"score_spread":0.3151176201463158,"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."}}