{"id":"W4410038668","doi":"10.3390/computers14050171","title":"ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Computers","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Stacking; Artificial intelligence; Pattern recognition (psychology); Computer science; Transformer; Machine learning; Computer vision; Engineering; Physics; Nuclear magnetic resonance; 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":[0.0001323505,0.0001705193,0.0001632389,0.00002785919,0.0003963135,0.0001515564,0.000143076,0.00006061871,0.000009228898],"category_scores_gemma":[0.000006170001,0.00006577763,0.00006302453,0.000328424,0.00005754349,0.0003339591,0.00002222345,0.0000735067,0.000002843269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002386495,"about_ca_system_score_gemma":0.00001057136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002851282,"about_ca_topic_score_gemma":0.00009513678,"domain_scores_codex":[0.9989469,0.00002068658,0.0001845809,0.0004153164,0.000123695,0.0003088336],"domain_scores_gemma":[0.9996092,0.0000775295,0.00004917694,0.00005410481,0.00008418402,0.0001257768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000394804,0.0002767087,0.0001232764,0.00008524006,0.00001998958,0.000001458951,0.0004112551,0.0003087427,0.06246756,0.000528418,0.0003012855,0.9350812],"study_design_scores_gemma":[0.002637102,0.01181433,0.08407579,0.002277903,0.0006834597,0.00003402494,0.0295702,0.2011656,0.6061354,0.02463582,0.03224112,0.004729291],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.771268,0.0000534743,0.226924,0.0003863239,0.0001082429,0.0006219022,0.00001656182,0.00006135799,0.0005602015],"genre_scores_gemma":[0.9956957,0.00002440818,0.003223701,0.000339545,0.0001597485,0.00008780857,0.0003559002,0.000001535113,0.000111702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.930352,"threshold_uncertainty_score":0.3048163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02422219827705967,"score_gpt":0.2407985961309776,"score_spread":0.2165763978539179,"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."}}