{"id":"W2012655519","doi":"10.1088/0004-6256/135/1/414","title":"CLEANING THE USNO-B CATALOG THROUGH AUTOMATIC DETECTION OF OPTICAL ARTIFACTS","year":2007,"lang":"en","type":"article","venue":"The Astronomical Journal","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Spurious relationship; Stars; Sky; Halo; Virtual observatory; Physics; Computer science; Outlier; Astrophysics; Reflection (computer programming); Diffraction; Astronomy; Artificial intelligence; Optics; Galaxy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00135102,0.00009531853,0.0001248417,0.00005034334,0.0003151978,0.0001514845,0.0007670854,0.00004336726,0.00001144934],"category_scores_gemma":[0.00007438201,0.00005241489,0.0001152127,0.0001649525,0.0001426677,0.0004352219,0.000156485,0.0004886067,0.00003194971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008632625,"about_ca_system_score_gemma":0.00004644031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002053092,"about_ca_topic_score_gemma":0.00000801328,"domain_scores_codex":[0.9989262,0.00008998586,0.0003795811,0.0001145871,0.0002027946,0.0002868978],"domain_scores_gemma":[0.9990972,0.0002517416,0.0001989279,0.0003366401,0.00005544264,0.0000600033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004279568,0.00006688144,0.0002521955,0.000004655197,0.00005637229,0.000006471351,0.001601937,0.0002148836,0.05500941,0.002959752,0.0001817194,0.9396029],"study_design_scores_gemma":[0.0002078802,0.0002291615,0.02836036,0.00002053708,0.00001916216,0.0004934585,0.0002963016,0.006846896,0.9570399,0.005814207,0.0005639855,0.0001081479],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.389724,0.00001775503,0.6093952,0.0003800061,0.0001578777,0.00005380514,1.001137e-7,0.00005087651,0.0002203312],"genre_scores_gemma":[0.9715336,0.00000244748,0.02812327,0.0001015769,0.000217649,0.000001655481,9.676655e-8,0.000006423325,0.00001330318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9394948,"threshold_uncertainty_score":0.2424279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175022818729697,"score_gpt":0.24834364873879,"score_spread":0.236593420551493,"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."}}