{"id":"W4284965896","doi":"10.1364/ol.457142","title":"Time-domain feature extraction for target-specificity in Photoacoustic Remote Sensing Microscopy","year":2022,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Illumisonics (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Frontiers Foundation; Mitacs; University of Waterloo; Canada Foundation for Innovation; illumiSonics","keywords":"SIGNAL (programming language); Absorption (acoustics); Photoacoustic imaging in biomedicine; Time domain; Computer science; Cluster analysis; Materials science; Microscopy; Nanosecond; Artificial intelligence; Biological system; Biomedical engineering; Computer vision; Pattern recognition (psychology); Acoustics; Optics; Laser; Physics; Biology; Engineering","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":[0.0002284496,0.0001799293,0.0001944884,0.0001935621,0.0002516534,0.00002038784,0.0001667651,0.00006750369,0.0001185246],"category_scores_gemma":[0.00002178003,0.0002459921,0.00008917882,0.0005031622,0.00004450892,0.0001510249,0.00004658898,0.0004194947,0.00001435881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006364541,"about_ca_system_score_gemma":0.00003830804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000450493,"about_ca_topic_score_gemma":0.00001232167,"domain_scores_codex":[0.9990211,0.00004964896,0.0001227202,0.0003533093,0.00006150897,0.0003917606],"domain_scores_gemma":[0.9994868,0.0001433984,0.00004508751,0.0002261168,0.00002896113,0.00006964808],"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":[0.0001078265,0.00002537472,0.00005204809,0.00004352596,0.00002594986,0.0001969705,0.0002696089,0.8145545,0.1820859,0.00009778347,0.002177868,0.0003626594],"study_design_scores_gemma":[0.0007533117,0.00002486971,0.00009388102,0.00001929967,0.00002878971,0.00003691599,0.001132709,0.9867821,0.004530713,0.001437502,0.00488105,0.0002789008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5196446,0.00005407917,0.4785913,0.00002108418,0.000331532,0.0002570865,0.00005595588,0.0001834351,0.0008610064],"genre_scores_gemma":[0.99143,0.00001764008,0.007016718,0.00005268281,0.00005497229,3.912074e-7,0.00003212057,0.00004195491,0.001353494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4717855,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789540650036301,"score_gpt":0.1755308652639695,"score_spread":0.1576354587636065,"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."}}