{"id":"W2962345832","doi":"10.1364/boe.10.003938","title":"Elimination of imaging artifacts in second harmonic generation microscopy using interferometry","year":2019,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Armand Frappier Museum; Wilfrid Laurier University; University of Ottawa; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Université Paris-Saclay; Canada Foundation for Innovation","keywords":"Optics; Microscopy; Interferometry; Second-harmonic generation; Phase imaging; Medical imaging; Image processing; Multiphoton fluorescence microscope; Interference microscopy; Materials science; Computer science; Fluorescence microscope; Physics; Computer vision; Artificial intelligence; Laser; Fluorescence; Image (mathematics)","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":[0.0001780072,0.0001263392,0.000158896,0.0001540647,0.0000178922,0.0000167302,0.0001819671,0.000143902,0.00004389225],"category_scores_gemma":[0.00006745513,0.0001311481,0.00004182494,0.0001478845,0.0001309315,0.0000148814,0.0001374663,0.000107888,0.000004003577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004408794,"about_ca_system_score_gemma":0.0000555241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006127691,"about_ca_topic_score_gemma":0.000002400558,"domain_scores_codex":[0.9989783,0.00003655407,0.0003243366,0.0003010154,0.0001447,0.0002151543],"domain_scores_gemma":[0.9994659,0.00001094351,0.0001217934,0.0002691509,0.00007423436,0.00005798855],"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":[0.00002843157,0.00007560727,0.001713483,0.00004284427,0.000006011634,0.000001630877,0.00004068728,0.00001347847,0.9970992,0.00002116638,0.0001064665,0.0008510163],"study_design_scores_gemma":[0.0003354924,0.000118909,0.0003453807,0.00007635945,0.000004426318,0.000005315406,0.00004081854,0.00527037,0.9929714,0.0000271607,0.0006692784,0.0001350532],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8847297,0.0002667744,0.1145306,0.00002767572,0.0001851923,0.000184094,0.00001599275,0.00001013466,0.00004981323],"genre_scores_gemma":[0.9116601,0.00004560399,0.08796901,0.0000642183,0.00007204421,0.000006604301,0.0001119717,0.00001958388,0.00005082691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02693041,"threshold_uncertainty_score":0.5348065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01332467602695652,"score_gpt":0.3016004505450558,"score_spread":0.2882757745180993,"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."}}