{"id":"W2949127041","doi":"10.1101/406108","title":"Aperture Phase Modulation with Adaptive Optics: A Novel Approach for Speckle Reduction and Structure Extraction in Optical Coherence Tomography","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Eye Institute; University of California, Davis; National Institutes of Health; National Science Foundation","keywords":"Speckle pattern; Optical coherence tomography; Speckle noise; Optics; Adaptive optics; Numerical aperture; Speckle imaging; Aperture (computer memory); Point spread function; Phase modulation; Coherence (philosophical gambling strategy); Modulation (music); Noise (video); Physics; Phase noise; Computer science; Artificial intelligence; Wavelength; Image (mathematics); Acoustics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002038433,0.0005590284,0.0004666691,0.0004487525,0.0001155916,0.0001884576,0.000253104,0.0006889125,0.000008914988],"category_scores_gemma":[0.00005155064,0.0005735819,0.00008819644,0.0008433263,0.0002696957,0.0003199922,0.0000720791,0.0008574522,0.000001499458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000183488,"about_ca_system_score_gemma":0.0001046828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000997004,"about_ca_topic_score_gemma":0.000003201765,"domain_scores_codex":[0.9978263,0.00002608454,0.0004428083,0.0009574028,0.0002863026,0.0004611265],"domain_scores_gemma":[0.9984366,0.00007993344,0.0001678743,0.0006977048,0.000397998,0.0002198304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002873679,0.000539216,0.0007194366,0.0007569868,0.000281542,0.000003376148,0.00004545153,0.02618304,0.9668362,0.004263878,0.00004533756,0.00003816282],"study_design_scores_gemma":[0.003161434,0.0005536499,0.05361263,0.0005515571,0.0003496336,5.359532e-7,0.0000475679,0.8618712,0.07760704,0.00009206294,0.0001989181,0.001953797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5713984,0.0001877422,0.4251313,0.0000317689,0.0001986511,0.002139888,0.000479428,0.0004065997,0.00002624813],"genre_scores_gemma":[0.7495797,0.00002815046,0.2492585,0.000007772866,0.0002734347,0.0007522791,0.000004799323,0.00009490939,4.391776e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8892292,"threshold_uncertainty_score":0.9996716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636522306391053,"score_gpt":0.2324703585138359,"score_spread":0.2161051354499253,"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."}}