{"id":"W4410190620","doi":"10.1002/smll.202412271","title":"Design Principles of Nanosensors for Multiplex Detection of Contaminants in Food","year":2025,"lang":"en","type":"review","venue":"Small","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Senior Talent Foundation of Jiangsu University; Natural Science Foundation of Jiangsu Province; Fonds der Chemischen Industrie","keywords":"Nanosensor; Multiplex; Computer science; Food safety; Nanotechnology; Biochemical engineering; Risk analysis (engineering); Materials science; Engineering; Business; Medicine; Bioinformatics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009882819,0.001574484,0.00106789,0.002613377,0.0003949798,0.000996051,0.00147682,0.001925692,0.001556968],"category_scores_gemma":[0.0005952221,0.001026136,0.0008183854,0.001477304,0.001149365,0.00182159,0.0008878866,0.002800836,0.002272285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092776,"about_ca_system_score_gemma":0.000732451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004679001,"about_ca_topic_score_gemma":0.0007073972,"domain_scores_codex":[0.9994817,0.00006888413,0.00004242667,0.0001104581,0.0002566319,0.00003993127],"domain_scores_gemma":[0.9998277,0.00007309162,0.00002072427,0.000009301778,0.00005716351,0.00001186787],"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":[0.0000784167,0.0001817893,0.0002570167,0.02318213,0.000161197,0.0007144414,0.0003509078,0.00442126,0.1239088,0.1497157,0.02404377,0.6729846],"study_design_scores_gemma":[0.00001519489,0.0001401971,0.0002907648,0.001273026,0.00004914027,0.001273262,0.00004364703,0.001570261,0.03579805,0.01485958,0.9446201,0.00006664765],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001158597,0.9340219,0.04849797,0.001390772,0.001203433,0.0001757607,0.0001233928,0.0001625477,0.01326549],"genre_scores_gemma":[0.01024026,0.9437682,0.03382046,0.001183192,0.0003814999,0.0004060641,0.0001249905,0.00002767561,0.01004766],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002613377,"threshold_uncertainty_score":0.007928669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06941386573642412,"score_gpt":0.3311134782403581,"score_spread":0.261699612503934,"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."}}