{"id":"W4380901791","doi":"10.1039/d3lc00125c","title":"A competitive, bead-based assay combined with microfluidics for multiplexed toxin detection","year":2023,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Centre for Bioengineering and Biotechnology, University of Waterloo; University of Waterloo; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Microfluidics; Bead; Multiplexing; Magnetic bead; Toxin; Chromatography; Nanotechnology; Chemistry; Materials science; Engineering; Biochemistry; Electronic engineering","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":[0.0003490213,0.0002098827,0.0001952828,0.0002786741,0.0001015459,0.00002850328,0.0001181539,0.0001125855,0.00001371225],"category_scores_gemma":[0.0000547869,0.000193825,0.00004623401,0.001053376,0.00005664235,0.0000557125,0.00001331517,0.0001858355,0.0000485622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350753,"about_ca_system_score_gemma":0.00002864831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004363726,"about_ca_topic_score_gemma":0.000004193286,"domain_scores_codex":[0.9990816,0.0000225607,0.0002345743,0.0002201022,0.0001511123,0.0002900325],"domain_scores_gemma":[0.9994095,0.0001317966,0.00004746355,0.0002259334,0.0001545105,0.0000308223],"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":[0.0002389363,0.00003823388,0.00007376826,0.00009467431,0.00003687272,0.000004126515,0.00008197103,0.00009748518,0.9715838,0.01556314,0.005481085,0.006705859],"study_design_scores_gemma":[0.00159461,0.0004333681,0.0008306958,0.00007967975,0.00001094136,0.000001907458,0.00005667885,0.008256283,0.9515836,0.0002612354,0.03663958,0.0002513782],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3359918,0.00004318881,0.6532521,0.0004447565,0.000468248,0.00158258,0.0002345771,0.003762249,0.004220448],"genre_scores_gemma":[0.9959783,0.000004095821,0.002445352,0.0004392285,0.00006330101,0.0002823582,0.000436126,0.00007560997,0.0002756274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6599864,"threshold_uncertainty_score":0.7903956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01496268970705325,"score_gpt":0.2341421265577187,"score_spread":0.2191794368506654,"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."}}