{"id":"W2964696490","doi":"10.1556/066.2019.48.3.12","title":"Applicability of ELISA methods for high gluten-containing samples","year":2019,"lang":"en","type":"article","venue":"Acta Alimentaria","topic":"Celiac Disease Research and Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Leibniz-Gemeinschaft; Health Canada; Emberi Eroforrások Minisztériuma; Technische Universität München","keywords":"Gluten; Gliadin; Certified reference materials; Chromatography; Certification; Sample preparation; Computer science; Food science; Chemistry; Biochemical engineering; Biotechnology; Computational biology; Biology; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.007847765,0.001845443,0.0008150436,0.002468137,0.0006409623,0.001757584,0.001919511,0.002618698,0.001720636],"category_scores_gemma":[0.008889343,0.0008565987,0.0008433368,0.001263281,0.0009733699,0.001223539,0.001291503,0.002074614,0.001922826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005445007,"about_ca_system_score_gemma":0.0005617643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007559609,"about_ca_topic_score_gemma":0.0009890947,"domain_scores_codex":[0.9855605,0.004869181,0.0007877308,0.003057646,0.005223924,0.0005010304],"domain_scores_gemma":[0.9927459,0.00329744,0.0008377639,0.0009205995,0.002047175,0.000151142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002012131,0.0002276925,0.005637142,0.0009405025,0.0001614003,0.000244588,0.0002661289,0.0008592701,0.95421,0.0005092415,0.0006351984,0.0361076],"study_design_scores_gemma":[0.00002823033,0.0008532081,0.0131492,0.0002262519,0.0002039298,0.001923712,0.0002736975,0.0108992,0.9553123,0.001500615,0.01554359,0.00008603243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2466224,0.03985554,0.6957615,0.001334995,0.001568493,0.00109712,0.001362968,0.002248396,0.01014853],"genre_scores_gemma":[0.5351944,0.01429668,0.4385156,0.001451594,0.0005690839,0.0009530598,0.001308086,0.0002215619,0.007489797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007847765,"threshold_uncertainty_score":0.04150349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03407408998154587,"score_gpt":0.3952136689178143,"score_spread":0.3611395789362685,"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."}}