{"id":"W4413679634","doi":"10.1109/compsac65507.2025.00230","title":"An Amodal Segmentation Pipeline for Critical Infrastructure Asset Imaging","year":2025,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Amodal perception; Critical infrastructure; Pipeline (software); Computer science; Asset (computer security); Segmentation; Artificial intelligence; Computer security; Neuroscience; Psychology; Programming language","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":[0.00004255687,0.00005871331,0.00006267795,0.00002622645,0.00004420515,0.00003386414,0.00005676434,0.00002117471,0.00003633584],"category_scores_gemma":[0.00002850763,0.00005429552,0.00002394538,0.0000899783,0.00001405999,0.00008378411,0.000006487933,0.00005147442,0.000003504282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001430927,"about_ca_system_score_gemma":0.000005625301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000376446,"about_ca_topic_score_gemma":0.000001256877,"domain_scores_codex":[0.9996736,0.000007317798,0.00009041161,0.00009351939,0.00003261242,0.0001025428],"domain_scores_gemma":[0.9997241,0.00009914501,0.000003597217,0.00010482,0.00003465229,0.00003369017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000551215,0.00005287818,0.0006628584,0.0001329404,0.00001333319,2.347899e-7,0.00005392196,0.009968818,0.409202,0.2247822,0.01722341,0.3379019],"study_design_scores_gemma":[0.0001896987,0.000006853553,0.01058289,0.000006759228,0.00001986106,3.540689e-7,0.000114729,0.857761,0.01703953,0.11051,0.003666262,0.0001020997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01323456,0.00001108457,0.9826817,0.000596261,0.0001163368,0.000131601,0.00002201336,0.0001774232,0.003029008],"genre_scores_gemma":[0.7318346,8.352642e-7,0.2677556,0.0001982702,0.00007287337,0.0000386949,0.00002981893,0.000006665184,0.00006268615],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8477922,"threshold_uncertainty_score":0.2214107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006123010439203879,"score_gpt":0.3303561681936351,"score_spread":0.3242331577544312,"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."}}