{"id":"W2089076201","doi":"10.1111/j.1365-2818.2009.03155.x","title":"Extending immunofluorescence detection limits in whole paraffin‐embedded formalin fixed tissues using hyperspectral confocal fluorescence imaging","year":2009,"lang":"en","type":"article","venue":"Journal of Microscopy","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"Princess Margaret Hospital Foundation","keywords":"Autofluorescence; Hyperspectral imaging; Fluorescence; Fluorescence-lifetime imaging microscopy; Confocal; Ex vivo; Confocal microscopy; Microscopy; Chemical imaging; Fluorescence microscope; Excitation wavelength; Spectral imaging; Immunofluorescence; Materials science; Biomedical engineering; In vivo; Pathology; Optics; Medicine; Computer science; Biology; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004873834,0.0003083718,0.0003893373,0.0002569256,0.0001352992,0.00009169526,0.000466635,0.000181611,0.000007031986],"category_scores_gemma":[0.000162841,0.0003057926,0.0001706266,0.0002552161,0.0001579616,0.00009066967,0.00005823138,0.0004698341,0.000003233929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849476,"about_ca_system_score_gemma":0.0001481411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001864829,"about_ca_topic_score_gemma":0.000009867133,"domain_scores_codex":[0.9979848,0.00009901758,0.0007607514,0.0003505403,0.0002418416,0.0005630855],"domain_scores_gemma":[0.9988104,0.00001746285,0.0005105925,0.0003063543,0.0002354245,0.0001198155],"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":[0.0004669964,0.0001045604,0.001368566,0.00001096218,0.00001198284,0.00006057265,0.0001618515,0.0001429555,0.9876522,0.00001067682,0.0001518079,0.009856846],"study_design_scores_gemma":[0.0008409168,0.0005835592,0.001693534,0.0003068554,0.00001998461,0.0005347421,0.0002470033,0.0006855858,0.9939553,0.0001048916,0.0007105071,0.0003171476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.910685,0.004771503,0.08375134,0.0001903923,0.0003072018,0.0002323941,0.000006909332,0.00002760115,0.00002763119],"genre_scores_gemma":[0.8224677,0.0004905114,0.1765903,0.0001620689,0.0002217024,0.0000021076,0.000005984235,0.00002926505,0.00003029952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09283897,"threshold_uncertainty_score":0.9999394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031512920139291,"score_gpt":0.3153362361583054,"score_spread":0.3050211069569125,"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."}}