{"id":"W2149806886","doi":"10.1016/j.bios.2004.06.022","title":"Label-free detection of nucleic acid and protein microarrays by scanning Kelvin nanoprobe","year":2004,"lang":"en","type":"article","venue":"Biosensors and Bioelectronics","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nanoprobe; Nucleic acid; Protein microarray; Chemistry; DNA microarray; Nanotechnology; Nucleic acid quantitation; Biophysics; Analytical Chemistry (journal); Biochemistry; Materials science; Chromatography; Biology; Nanoparticle; Gene expression; Gene","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":[0.00008046411,0.0001528504,0.0001293633,0.00003377415,0.0001266638,0.00001818498,0.00009445337,0.0001676427,4.974819e-7],"category_scores_gemma":[0.00002584198,0.0001416145,0.00003136717,0.0001068651,0.0001688841,0.000004807918,0.00009134304,0.0000918167,3.723123e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002166195,"about_ca_system_score_gemma":0.00003438515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003281318,"about_ca_topic_score_gemma":0.00004477351,"domain_scores_codex":[0.9991909,0.00001471199,0.0001682704,0.0003216264,0.00006716292,0.0002373043],"domain_scores_gemma":[0.9995194,0.000002403232,0.0001018385,0.0002634964,0.00005461904,0.00005823835],"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":[0.00003521816,0.00004322124,0.00001509239,0.00001743169,0.00001322503,2.982275e-7,0.00001172303,5.930823e-7,0.9913273,0.0005304508,0.00009764804,0.007907781],"study_design_scores_gemma":[0.000538994,0.0006917548,0.00004162098,0.00002415736,0.00001219504,0.00002669286,0.00002507369,0.00001910865,0.9859433,0.001242179,0.01125712,0.0001778644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920384,0.003319407,0.003859468,0.0003709315,0.00001143934,0.0002849579,0.00003622373,0.000032348,0.00004685667],"genre_scores_gemma":[0.9877182,0.0009549983,0.01104543,0.00007772254,0.00003507877,0.00001323076,0.00002777685,0.00002157596,0.0001059704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01115947,"threshold_uncertainty_score":0.5774872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004887575976396363,"score_gpt":0.2205550093184522,"score_spread":0.2156674333420558,"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."}}