{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007162819,0.0008101916,0.0005462871,0.0006109579,0.0002796423,0.0004981713,0.0005451982,0.0007140947,0.000533671],"category_scores_gemma":[0.0008610951,0.000427279,0.0003375391,0.0005367581,0.0008837929,0.0006233176,0.0007869862,0.000772469,0.0003696941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003162768,"about_ca_system_score_gemma":0.0003993591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003381572,"about_ca_topic_score_gemma":0.0004323596,"domain_scores_codex":[0.9995661,0.00008217596,0.00001792113,0.00007770179,0.0001782759,0.00007784427],"domain_scores_gemma":[0.9996057,0.0001660607,0.00008922332,0.00006584251,0.00005528058,0.00001787626],"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":[0.00005577405,0.00001631996,0.0003547959,0.0001302816,0.000007829489,0.00007939756,0.00006849036,0.0004377985,0.984289,0.0009160535,0.00009320267,0.013551],"study_design_scores_gemma":[0.00001427926,0.0001730712,0.002169168,0.00001328744,0.00001477338,0.0002716863,0.00003014609,0.007041032,0.9871344,0.0006788111,0.002442624,0.00001666787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5148479,0.004192782,0.4747154,0.0004631068,0.000140889,0.0001364781,0.0001302071,0.001080616,0.004292719],"genre_scores_gemma":[0.7021614,0.003419588,0.2918231,0.0001858667,0.00008861898,0.0001616027,0.0001712,0.000191779,0.001796882],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0008101916,"threshold_uncertainty_score":0.003788114,"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."}}