{"id":"W2011050789","doi":"10.1109/embc.2012.6346933","title":"Saliency-guided compressive fluorescence microscopy","year":2012,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microscopy; Fluorescence microscope; Compressed sensing; Fluorescence; Materials science; Fluorescence-lifetime imaging microscopy; Sampling (signal processing); Optical microscope; Biomedical engineering; Computer science; Artificial intelligence; Computer vision; Optics; Physics; Scanning electron microscope; Composite material; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.00004505901,0.0001291621,0.0001200756,0.00004636904,0.00004227959,0.00002410688,0.0001506039,0.00005774546,0.0001305575],"category_scores_gemma":[0.000008172728,0.0001165117,0.00003805584,0.00008793648,0.00003707259,0.0001699847,0.00004112821,0.00009721136,0.0001547156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002386144,"about_ca_system_score_gemma":0.000004125142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002340476,"about_ca_topic_score_gemma":9.80446e-7,"domain_scores_codex":[0.9993404,0.00001242634,0.0001313164,0.00009081118,0.00009523696,0.0003297892],"domain_scores_gemma":[0.9995841,0.00002332016,0.0000153331,0.0002542101,0.00002990559,0.00009307251],"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":[0.000002905886,0.00003712727,0.00349431,0.00001206393,0.00002544565,0.000004239267,0.0003069163,0.000343247,0.7933344,0.002782945,0.1965674,0.003088961],"study_design_scores_gemma":[0.00008596084,0.000008607758,0.002292247,0.00003342186,0.000007836198,0.00002065636,0.00002684732,0.005659363,0.9761543,0.0002378644,0.01526022,0.0002126865],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7037314,0.002142899,0.0769559,0.00007687514,0.001761559,0.0003772214,0.000007145615,0.005882647,0.2090644],"genre_scores_gemma":[0.9748423,0.00005136703,0.02464106,0.0001097086,0.0001373859,0.000007374588,0.000003052732,0.00002517681,0.0001825674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2711109,"threshold_uncertainty_score":0.4751211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160926211185458,"score_gpt":0.265398386028337,"score_spread":0.2437891239164825,"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."}}