{"id":"W2032868151","doi":"10.1117/12.487794","title":"Development of a coded-aperture backscatter imager using the UC San Diego HEXIS detector","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"University of California, San Diego; Defence Research and Development Canada; National Aeronautics and Space Administration","keywords":"Detector; Coded aperture; Payload (computing); Aperture (computer memory); Computer science; Satellite; Explosive material; Remote sensing; Backscatter (email); Optics; Physics; Systems engineering; Aerospace engineering; Engineering; Telecommunications; Geology; Geography; Acoustics; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004918012,0.0003944833,0.0004370096,0.00008962591,0.0001313435,0.00007611227,0.0007581204,0.0001612683,0.0000417612],"category_scores_gemma":[0.0003503327,0.0002952462,0.0004813085,0.0003584519,0.0002210075,0.0004981506,0.000107178,0.0004087431,0.000001775659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000196215,"about_ca_system_score_gemma":0.00004063738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001880762,"about_ca_topic_score_gemma":3.157471e-7,"domain_scores_codex":[0.9978182,2.721286e-8,0.0007888005,0.0003083029,0.0006007987,0.0004838553],"domain_scores_gemma":[0.9985645,0.0001621298,0.0002564451,0.0000835931,0.0008276209,0.000105736],"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":[0.00003189235,0.00005994279,0.0002165284,0.0005846188,0.0004762024,1.040853e-7,0.0008119416,0.001501693,0.9509312,0.04401498,0.00092541,0.0004455224],"study_design_scores_gemma":[0.001047331,0.00007835632,0.0003875085,0.0005196084,0.0001805663,0.00003626783,0.003365563,0.06256884,0.9133044,0.0006945405,0.0171733,0.0006436966],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952776,0.0002940449,0.001519219,0.0002417992,0.0002472536,0.0004281749,0.00002604734,0.0001052027,0.001860674],"genre_scores_gemma":[0.5983364,0.00004039916,0.4010222,0.0001186637,0.0001872302,0.00009781294,0.000004270329,0.0001084328,0.00008460374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.399503,"threshold_uncertainty_score":0.99995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203288175420587,"score_gpt":0.2292282314141379,"score_spread":0.2171953496599321,"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."}}