{"id":"W3097278061","doi":"10.1016/j.tifs.2020.10.035","title":"Advances in epitope mapping technologies for food protein allergens: A review","year":2020,"lang":"en","type":"review","venue":"Trends in Food Science & Technology","topic":"Food Allergy and Anaphylaxis Research","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Anhui Provincial Key Research and Development Plan; National Natural Science Foundation of China","keywords":"Epitope; Food allergy; Hypoallergenic; Allergen; Food allergens; Epitope mapping; Computational biology; Identification (biology); Allergy; Food protein; Biotechnology; Computer science; Immunology; Medicine; Biology; Antigen; Food science","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":["metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001139155,0.0006367007,0.003052014,0.008810095,0.0001499594,0.00002818722,0.002159079,0.0008598551,0.0000317502],"category_scores_gemma":[0.002536062,0.000504892,0.0004281054,0.02298948,0.001846783,0.0002623041,0.0008063671,0.001904226,0.00002580877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004538258,"about_ca_system_score_gemma":0.0009778705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000175248,"about_ca_topic_score_gemma":0.0002287568,"domain_scores_codex":[0.9950624,0.00007254494,0.001264147,0.001627463,0.0006356013,0.001337865],"domain_scores_gemma":[0.99814,0.00008880709,0.0004021031,0.001097918,0.0001547519,0.0001163896],"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":[0.0000122065,0.0001119573,0.000002303771,0.02043377,0.0000357174,0.00005982654,0.00002201653,1.486142e-7,0.00003255006,0.002988585,0.00009823075,0.9762027],"study_design_scores_gemma":[0.0004740359,0.003014186,9.321193e-7,0.06673516,0.00001834571,0.0001203542,0.0001969283,0.00000870708,0.000139079,0.00171019,0.927159,0.0004231557],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001730991,0.9862155,0.00003051628,0.009166135,0.0001076807,0.002791236,0.00003838371,0.0005543964,0.001078837],"genre_scores_gemma":[0.001410625,0.9922082,0.002436599,0.00006297391,0.00003770631,0.003678078,0.00002727723,0.0000544173,0.00008416663],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9757795,"threshold_uncertainty_score":0.9997402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09899186295615733,"score_gpt":0.4054280556490676,"score_spread":0.3064361926929103,"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."}}