{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003594876,0.0002187158,0.0002377844,0.0003961648,0.0001195013,0.000435886,0.0002465122,0.0004352833,0.0007347378],"category_scores_gemma":[0.0009255744,0.0001872158,0.0002056285,0.0003269668,0.0006586334,0.0007707065,0.0002511746,0.000398271,0.0001607784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002210053,"about_ca_system_score_gemma":0.0001943011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003416991,"about_ca_topic_score_gemma":0.0003898833,"domain_scores_codex":[0.9998567,0.00003791453,0.000006731422,0.0000302794,0.00005571103,0.00001275954],"domain_scores_gemma":[0.999273,0.0004603731,0.00007457646,0.00008360678,0.00008174343,0.00002676105],"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":[0.00006865972,0.0000871481,0.003031478,0.0001416366,0.00001660339,0.0004084343,0.0002485743,0.02930922,0.8882908,0.0342528,0.0003182374,0.04382635],"study_design_scores_gemma":[0.00001924415,0.0003758906,0.01617405,0.00006830802,0.00002895528,0.002526956,0.0002848813,0.5264239,0.3809958,0.06243,0.01058654,0.00008542217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2959005,0.003337145,0.6918809,0.0006986503,0.00005716267,0.00002895894,0.0001528239,0.000389553,0.007554265],"genre_scores_gemma":[0.8939702,0.002705311,0.1012888,0.0001406663,0.0000442189,0.00002969224,0.0001171449,0.00004086894,0.001663143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007347378,"threshold_uncertainty_score":0.002457976,"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."}}