{"id":"W4409419075","doi":"10.3847/2515-5172/adcb3a","title":"Transit-APP: A Centroid-free Method of Identifying Background Transit False Positives","year":2025,"lang":"en","type":"article","venue":"Research Notes of the AAS","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"False positive paradox; Transit (satellite); Centroid; Computer science; Artificial intelligence; Public transport; Engineering; Transport engineering","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":[0.002591001,0.0001249799,0.0002847453,0.000254306,0.0002147952,0.0001276913,0.003061955,0.00008462417,0.000008289649],"category_scores_gemma":[0.0004464666,0.0000966412,0.0002010639,0.001203225,0.0002656056,0.0002882272,0.0006204898,0.0004319604,0.000004940519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000426643,"about_ca_system_score_gemma":0.0002743897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005816614,"about_ca_topic_score_gemma":0.00006573484,"domain_scores_codex":[0.9971703,0.000844999,0.0003557773,0.0003754235,0.0008079766,0.0004454902],"domain_scores_gemma":[0.9967283,0.001392422,0.00007415788,0.001273249,0.0004547148,0.00007716443],"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":[0.0001587818,0.0003628866,0.0005247061,0.0006392793,0.000258087,0.00001070008,0.004110662,0.0029492,0.4832142,0.4058332,0.0006845775,0.1012537],"study_design_scores_gemma":[0.001100506,0.0001930078,0.007084893,0.001073829,0.00004719275,0.00001438933,0.0004342528,0.09385584,0.5829223,0.3128697,0.0001421234,0.0002619752],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0348507,0.0008403294,0.9563888,0.006428444,0.0001062454,0.0002349854,0.00001162723,0.00003649195,0.001102423],"genre_scores_gemma":[0.9246708,0.00007234928,0.07488709,0.00007407411,0.00001773631,0.000008141788,6.060792e-7,0.000007300262,0.0002619386],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.88982,"threshold_uncertainty_score":0.5689925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.152268412952173,"score_gpt":0.4373000677130633,"score_spread":0.2850316547608903,"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."}}