{"id":"W4393900669","doi":"10.1002/jbio.202300565","title":"A comparative study of <scp>CARE 2D</scp> and <scp>N2V 2D</scp> for tissue‐specific denoising in second harmonic generation imaging","year":2024,"lang":"en","type":"article","venue":"Journal of Biophotonics","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital; Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canada Foundation for Innovation; Canadian Cancer Society; McGill University; Faculty of Medicine, McGill University; Natural Sciences and Engineering Research Council of Canada","keywords":"Second-harmonic generation; Microscopy; Image restoration; Materials science; Computer science; Artificial intelligence; Noise (video); Second-harmonic imaging microscopy; Laser; Noise reduction; Image processing; Biomedical engineering; Image (mathematics); Optics; Medicine; Physics","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":[0.000348203,0.0002223347,0.0003898389,0.0002272377,0.00008404428,0.0001046889,0.0002028185,0.000129123,0.000001867441],"category_scores_gemma":[0.00008093766,0.0002109822,0.00009466859,0.0001946768,0.00009662131,0.00003727668,0.00009400874,0.0002756448,5.820992e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001046584,"about_ca_system_score_gemma":0.000158919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004880344,"about_ca_topic_score_gemma":0.00006602659,"domain_scores_codex":[0.9985408,0.00006709676,0.0006097083,0.0003471173,0.0001723472,0.0002629672],"domain_scores_gemma":[0.9989958,0.00008251824,0.0003328738,0.0002067654,0.0003068058,0.00007518254],"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":[0.00001444233,0.0001068794,0.000787076,0.00009352647,0.00005573237,0.00002942306,0.0037548,0.00005637458,0.9924248,0.000009690131,0.001641376,0.001025899],"study_design_scores_gemma":[0.0007207431,0.001068373,0.0004339494,0.000145946,0.0000436441,0.0000908392,0.007454257,0.0007187703,0.9710276,0.00007476389,0.01816665,0.00005451817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568748,0.03236406,0.009868659,0.0000122591,0.0002310393,0.0005599143,0.00003571939,0.0000114409,0.00004214095],"genre_scores_gemma":[0.9786062,0.001235123,0.01981081,0.00003041711,0.0001555398,0.00001991607,0.00002439277,0.00003606007,0.00008151256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03112894,"threshold_uncertainty_score":0.8603604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01965730017557633,"score_gpt":0.3183496869046462,"score_spread":0.2986923867290698,"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."}}