{"id":"W3006429358","doi":"10.1016/j.foodchem.2020.126396","title":"Development of paper-based microfluidic device for the determination of nitrite in meat","year":2020,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nitrite; Microfluidics; Food science; Nanotechnology; Chemistry; Chromatography; Biochemical engineering; Materials science; Engineering; Organic chemistry; Nitrate","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00002847261,0.00004738127,0.00007444109,0.000006544155,0.00001008421,0.000002333303,0.00005163321,0.00004471002,0.00001014771],"category_scores_gemma":[0.00002586348,0.00003961601,0.00003050523,0.00008572857,0.0000101506,0.00001170861,0.000005466415,0.00004436267,4.286306e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001273232,"about_ca_system_score_gemma":0.000009715861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.375251e-7,"about_ca_topic_score_gemma":0.000002018174,"domain_scores_codex":[0.9996824,0.000001599393,0.0001538172,0.00005358729,0.00005082243,0.0000578309],"domain_scores_gemma":[0.9998548,0.00003839247,0.00001990868,0.00004749039,0.00002196229,0.00001742574],"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":[0.000008383111,0.000006968599,0.00001539889,0.0004646515,0.000007188264,7.971347e-8,0.00006269418,0.00005926493,0.9923804,8.243549e-7,0.00002590056,0.006968185],"study_design_scores_gemma":[0.0002066019,0.00001164062,0.00008010633,0.0000272536,0.000008808669,1.44641e-7,0.00004787488,0.01838373,0.9786317,0.000004879245,0.002554657,0.00004261339],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904767,0.0006128965,0.008492358,0.0001258733,0.00001543761,0.00007720713,0.00001143987,0.00002173369,0.0001663739],"genre_scores_gemma":[0.9983857,0.00001097109,0.001538926,0.00002521686,0.00001779301,0.000006772933,0.000004322508,0.000006389915,0.000003939616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01832447,"threshold_uncertainty_score":0.1615494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02105253893360393,"score_gpt":0.2131906519263655,"score_spread":0.1921381129927615,"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."}}