{"id":"W3131464794","doi":"10.1016/j.bpj.2020.11.2193","title":"Machine Learning Enabled Phase Unwrapping for Digitalholographic Microscopy","year":2021,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Digital Holography and Microscopy","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robustness (evolution); Holography; Computer science; Speckle pattern; Convolutional neural network; Speckle noise; Digital holography; Artificial intelligence; Microscopy; Optics; Computation; Phase (matter); Biological system; Materials science; Computer vision; Algorithm; Physics; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004760651,0.0003705457,0.0003967501,0.0005660553,0.0003268032,0.0007084085,0.0007300861,0.0006509105,0.003056449],"category_scores_gemma":[0.001857871,0.0002859598,0.000306884,0.0006109352,0.0003802266,0.001071362,0.001215806,0.001222499,0.001041612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003499747,"about_ca_system_score_gemma":0.0005634857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009870821,"about_ca_topic_score_gemma":0.001672757,"domain_scores_codex":[0.9997779,0.00004759265,0.00001243927,0.00004513258,0.00009164856,0.00002540788],"domain_scores_gemma":[0.9992728,0.0002767059,0.00007291491,0.0001666186,0.0001758556,0.00003518558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003947423,0.0001413483,0.0008013028,0.0002976779,0.00006007021,0.0001273519,0.0001053984,0.0631981,0.2561333,0.01308993,0.004854383,0.6607964],"study_design_scores_gemma":[0.00001230322,0.00004931202,0.0004145639,0.00001610534,0.000009987388,0.0001176593,0.0000157553,0.9161996,0.07066733,0.007519013,0.0049577,0.00002073653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02022591,0.0004850065,0.9754725,0.0002917293,0.00008761256,0.0000340414,0.000151306,0.001737181,0.001514764],"genre_scores_gemma":[0.2714275,0.0005781332,0.7236149,0.0001916446,0.0000648826,0.00007026583,0.0003398495,0.0002469023,0.003465946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003056449,"threshold_uncertainty_score":0.01022482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124330876142327,"score_gpt":0.2847918986746432,"score_spread":0.2723588110604105,"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."}}