{"id":"W2079903870","doi":"10.1364/boe.4.001584","title":"Tri-modal microscopy with multiphoton and optical coherence microscopy/tomography for multi-scale and multi-contrast imaging","year":2013,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; British Columbia Innovation Council; BCFRST Foundation","keywords":"Microscopy; Optical coherence tomography; Optics; Microscope; Materials science; Two-photon excitation microscopy; Fluorescence microscope; Fluorescence-lifetime imaging microscopy; Physics; Fluorescence","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":[0.0007170918,0.0005693348,0.0004112596,0.001222616,0.0004269327,0.0006896362,0.0006635613,0.001001797,0.002651155],"category_scores_gemma":[0.0006120636,0.0004006298,0.0004693776,0.001025617,0.0007525979,0.001644523,0.001256838,0.001092118,0.000824758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006723743,"about_ca_system_score_gemma":0.0005093829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005017988,"about_ca_topic_score_gemma":0.001067087,"domain_scores_codex":[0.9993666,0.00009934369,0.00003160391,0.0001194685,0.0003199083,0.00006322889],"domain_scores_gemma":[0.9995498,0.0001347384,0.0001023502,0.00008208626,0.00009263606,0.00003835714],"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":[0.00007060222,0.0000502689,0.0004789039,0.0004244399,0.00003434288,0.0001825768,0.00007653529,0.001500865,0.9377999,0.01158986,0.001918804,0.04587284],"study_design_scores_gemma":[0.00004043438,0.0003221324,0.003134327,0.00009832497,0.00006618611,0.003665238,0.00008933512,0.1052145,0.8302884,0.008851973,0.0480604,0.0001686684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07072255,0.01187886,0.9029121,0.0009985288,0.0002471518,0.0002351336,0.0003354107,0.002049365,0.01062098],"genre_scores_gemma":[0.2873346,0.004963454,0.7017627,0.0007687105,0.0001682244,0.000344407,0.0003239979,0.0001886364,0.004145239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002651155,"threshold_uncertainty_score":0.008868992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145693681268753,"score_gpt":0.2566679142569484,"score_spread":0.2452109774442609,"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."}}