{"id":"W2933591459","doi":"10.1016/j.pacs.2019.02.002","title":"Insights into photoacoustic speckle and applications in tumor characterization","year":2019,"lang":"en","type":"article","venue":"Photoacoustics","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ted Rogers Centre for Heart Research; Toronto Metropolitan University; St. Michael's Hospital","funders":"Terry Fox Research Institute; Terry Fox Foundation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Speckle pattern; Optics; Superposition principle; Scattering; Materials science; Ultrasound; Speckle noise; Ultrasonic sensor; Photoacoustic imaging in biomedicine; Resolution (logic); Image resolution; Acoustics; Physics; Computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007043873,0.0002426035,0.0002704573,0.0002170907,0.00006173638,0.00005811452,0.0001485899,0.00007536969,0.00007805917],"category_scores_gemma":[0.00002775771,0.0002539486,0.00002980845,0.0003784922,0.00005214821,0.0001847768,0.00003829878,0.0002648174,0.00006579901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001370669,"about_ca_system_score_gemma":0.00003557453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002780668,"about_ca_topic_score_gemma":0.00001357773,"domain_scores_codex":[0.9988832,0.00001427691,0.0003137488,0.0002979713,0.0001729806,0.0003178166],"domain_scores_gemma":[0.9993894,0.0001201871,0.00004524586,0.0002932595,0.00004165139,0.0001103042],"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":[0.000006889232,0.00005617734,0.003529898,0.0004038658,0.00001873912,0.00001151305,0.000870865,0.01116438,0.9827845,0.0003704263,0.00004481481,0.0007379404],"study_design_scores_gemma":[0.0007429147,0.00002844694,0.01459948,0.0001092284,0.0000547293,0.00002049029,0.0007289042,0.9571723,0.0210612,0.001061525,0.003838569,0.0005821926],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8893731,0.000257618,0.1063664,0.000005771835,0.0003365054,0.000724824,0.00003107693,0.0002620622,0.002642557],"genre_scores_gemma":[0.9986048,0.0001340854,0.0006615443,0.0001130109,0.00009883917,0.0001101375,0.00008553452,0.00006186217,0.0001302084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9617233,"threshold_uncertainty_score":0.9999913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003795096781766635,"score_gpt":0.1894321981412919,"score_spread":0.1856371013595253,"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."}}