{"id":"W2007650467","doi":"10.3390/s100301743","title":"Improving the Ability of Image Sensors to Detect Faint Stars and Moving Objects Using Image Deconvolution Techniques","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Ministerio de Ciencia e Innovación","keywords":"Deconvolution; Stars; Artificial intelligence; Computer vision; Space debris; Aperture (computer memory); Image (mathematics); Physics; Computer science; Image restoration; Telescope; Image sensor; SIGNAL (programming language); Remote sensing; Noise (video); Image processing; Optics; Astronomy; Geology; Acoustics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006012268,0.000629592,0.0005740413,0.0005195182,0.000269236,0.0007397425,0.0006073837,0.0008690705,0.001724376],"category_scores_gemma":[0.001620975,0.0002801389,0.0003456634,0.0003459047,0.0008509175,0.001710664,0.0008057492,0.0007107567,0.001092075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000275573,"about_ca_system_score_gemma":0.0002681515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004125735,"about_ca_topic_score_gemma":0.0003856724,"domain_scores_codex":[0.9996589,0.00003414792,0.00001827238,0.0001051899,0.0001491981,0.00003433349],"domain_scores_gemma":[0.9993278,0.0003338651,0.00009344439,0.000104481,0.0001146846,0.00002583681],"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":[0.0001721028,0.00006910631,0.001643563,0.0003597079,0.00004974822,0.0001136062,0.0001736645,0.007632638,0.8439031,0.006408553,0.0005241239,0.13895],"study_design_scores_gemma":[0.00001808238,0.0001696645,0.002469599,0.00003288967,0.00006958111,0.0006743727,0.00006114518,0.06838361,0.9149703,0.003237004,0.009847299,0.00006639938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09571185,0.001825077,0.896639,0.00026021,0.00007470266,0.00004736012,0.00005143653,0.001150013,0.004240321],"genre_scores_gemma":[0.4013343,0.002081805,0.5915555,0.0003150022,0.00006499431,0.00005243915,0.000131277,0.0001758522,0.004288909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001724376,"threshold_uncertainty_score":0.005768538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005295201805301757,"score_gpt":0.2201935460773217,"score_spread":0.2148983442720199,"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."}}