{"id":"W4322496914","doi":"10.1089/derm.2022.0042","title":"Can You Picture It? Using Smartphone Artificial Intelligence to Identify Allergenic Plants","year":2023,"lang":"en","type":"article","venue":"Dermatitis","topic":"Allergic Rhinitis and Sensitization","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Allergic contact dermatitis; Contact dermatitis; Library science; Family medicine; Dermatology; Computer science; Allergy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001869164,0.0003842736,0.0002516338,0.0005123985,0.0001605749,0.0008942705,0.0001602296,0.0004873703,0.00416776],"category_scores_gemma":[0.001064064,0.0001081215,0.0002791971,0.0002733255,0.00009839236,0.0006141447,0.0003224507,0.0002893026,0.001903233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001207523,"about_ca_system_score_gemma":0.0001108602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001610754,"about_ca_topic_score_gemma":0.003869795,"domain_scores_codex":[0.9998939,0.00002452634,0.000008172815,0.00002891667,0.0000320323,0.00001230676],"domain_scores_gemma":[0.9997429,0.0001261147,0.00003933777,0.00001901416,0.00005445801,0.00001817931],"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":[0.001534214,0.0006316534,0.1173779,0.0007426891,0.0002555133,0.002038829,0.001366136,0.002626353,0.06250093,0.001016236,0.0243824,0.7855273],"study_design_scores_gemma":[0.0002834901,0.003600341,0.4956726,0.0008345885,0.00122922,0.01210316,0.009865682,0.2672643,0.0832792,0.01348963,0.1120735,0.0003042867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8906668,0.006211238,0.04931952,0.003493693,0.0007451787,0.0003319181,0.00280538,0.002101497,0.04432492],"genre_scores_gemma":[0.9382052,0.002986172,0.04583754,0.001143581,0.000204993,0.00006256806,0.0007956836,0.00005209318,0.01071217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00416776,"threshold_uncertainty_score":0.01394254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06093340076628207,"score_gpt":0.3282560300515704,"score_spread":0.2673226292852883,"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."}}