{"id":"W4313830032","doi":"10.32920/21842481","title":"Skull acoustic aberration correction in photoacoustic microscopy using a vector space similarity model: a proof-of-concept simulation study","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; St. Michael's Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; University of Illinois at Chicago; University of Illinois at Urbana-Champaign; National Institutes of Health; Wayne State University","keywords":"Skull; Imaging phantom; Similarity (geometry); Acoustics; Amplitude; Optics; Physics; Materials science; Biomedical engineering; Computer science; Computer vision; Anatomy; Biology; Image (mathematics); Engineering","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":[0.0005277553,0.0002731242,0.000259378,0.0001843191,0.0001831831,0.0004246388,0.0006276518,0.0006298759,0.001514937],"category_scores_gemma":[0.0008228068,0.0001322832,0.0002796047,0.0001692606,0.0003609943,0.0004139557,0.0002549622,0.000274315,0.0001760858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004808595,"about_ca_system_score_gemma":0.0005398422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003458455,"about_ca_topic_score_gemma":0.001399075,"domain_scores_codex":[0.9998591,0.00002890188,0.000003653256,0.000009986044,0.00008479981,0.00001356726],"domain_scores_gemma":[0.9995715,0.0001915272,0.00007948199,0.00003647953,0.0000969719,0.00002398559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003468273,0.0002231979,0.001540483,0.0002385697,0.00004678879,0.000412815,0.0001721067,0.8698248,0.0964582,0.00954841,0.0009825946,0.02020523],"study_design_scores_gemma":[0.00002990121,0.0001208919,0.0002280523,0.000005109906,0.000005895359,0.00006869205,0.00001265809,0.9828196,0.01596674,0.0001897942,0.0005454781,0.000007135038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.569485,0.000611958,0.4174677,0.0005950774,0.0001031619,0.0002804704,0.0001747175,0.0006564559,0.01062536],"genre_scores_gemma":[0.9509016,0.0001893214,0.04707073,0.00002563318,0.000005297793,0.00005562885,0.00003269354,0.00003666803,0.001682389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003458455,"threshold_uncertainty_score":0.006876647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04193114167848721,"score_gpt":0.3074775642780507,"score_spread":0.2655464225995635,"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."}}