{"id":"W4224097488","doi":"10.1101/2022.04.16.488489","title":"Wide-Field Multicontrast Nonlinear Microscopy for Histopathology","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; European Regional Development Fund; Lietuvos Mokslo Taryba","keywords":"Histology; Eosin; Microscopy; Materials science; Multiphoton fluorescence microscope; Second-harmonic generation; Optics; Histopathology; Microscope; High harmonic generation; Polarimetry; H&E stain; Fluorescence; Laser; Fluorescence microscope; Pathology; Staining; Physics; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001512673,0.001219796,0.0005789447,0.002088937,0.0007252168,0.0009075064,0.0008293798,0.0007680261,0.04112131],"category_scores_gemma":[0.001187167,0.0005489347,0.0003860627,0.0009045799,0.000509739,0.0009124185,0.00111022,0.001303198,0.01860538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007783696,"about_ca_system_score_gemma":0.000666933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005382338,"about_ca_topic_score_gemma":0.001409552,"domain_scores_codex":[0.9993861,0.0001790294,0.00002531899,0.00008439543,0.0002840994,0.00004100404],"domain_scores_gemma":[0.999115,0.0002236175,0.00004318605,0.0003498017,0.0002013765,0.00006687823],"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":[0.0001957018,0.0001150257,0.0008251268,0.0007552744,0.00005303136,0.000395162,0.0000960785,0.001489382,0.7670808,0.0479292,0.03773895,0.1433262],"study_design_scores_gemma":[0.00009408518,0.0002356257,0.0101341,0.0003162549,0.00007256873,0.005518788,0.0001146754,0.04961886,0.470237,0.05502712,0.4084763,0.0001546623],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01837691,0.007795393,0.9268289,0.001968371,0.001280595,0.0003837141,0.001965959,0.009554965,0.03184521],"genre_scores_gemma":[0.04797825,0.003645401,0.9185748,0.0002527419,0.0003946645,0.0003560118,0.001754376,0.00163128,0.02541246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04112131,"threshold_uncertainty_score":0.1375645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00859132543792484,"score_gpt":0.2643357750014758,"score_spread":0.2557444495635509,"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."}}