{"id":"W4403936830","doi":"10.1109/access.2024.3488730","title":"Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Identification (biology); PEST analysis; Computer science; Artificial intelligence; Agronomy; Biology; Ecology; Botany","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":[0.0002734903,0.000916964,0.0003679079,0.0007569058,0.0001544461,0.0004895409,0.0009627333,0.0009752626,0.001654494],"category_scores_gemma":[0.0004514042,0.0002941301,0.0006131261,0.0005459387,0.0001917779,0.0006514858,0.0006429909,0.0007141407,0.0004970002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006179299,"about_ca_system_score_gemma":0.0006619885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008705189,"about_ca_topic_score_gemma":0.01225442,"domain_scores_codex":[0.999889,0.00001348984,0.000005757366,0.00004018505,0.00002017974,0.00003143986],"domain_scores_gemma":[0.9998884,0.00002705455,0.00001521445,0.00001632702,0.00003972814,0.00001329247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000312614,0.0004979196,0.01148699,0.0002234955,0.0002533737,0.0003805022,0.00008816162,0.4319949,0.03110483,0.002487407,0.01150899,0.5096607],"study_design_scores_gemma":[0.000008110419,0.00005674042,0.00132046,0.00001723022,0.00002400488,0.00004388475,0.00001467607,0.9920056,0.003592549,0.001315964,0.001592891,0.000007823254],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3135316,0.002832667,0.6591807,0.0008316513,0.0002105326,0.0001918679,0.003532504,0.007192145,0.01249638],"genre_scores_gemma":[0.8492249,0.0007842365,0.1323118,0.0004262767,0.00004795892,0.0001233947,0.005113955,0.0001047014,0.01186282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008705189,"threshold_uncertainty_score":0.01730907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04594282142429996,"score_gpt":0.3012327862540143,"score_spread":0.2552899648297144,"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."}}