{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008503141,0.001010944,0.0006889635,0.002733118,0.000701429,0.001516694,0.001508612,0.001270854,0.007642326],"category_scores_gemma":[0.001602721,0.0006555848,0.001108268,0.001285468,0.0006237476,0.001800991,0.002051119,0.001270418,0.004589947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008872739,"about_ca_system_score_gemma":0.001519368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005258028,"about_ca_topic_score_gemma":0.01119455,"domain_scores_codex":[0.9994283,0.00005655041,0.0000272815,0.0001834827,0.0002063632,0.00009791632],"domain_scores_gemma":[0.9993082,0.000128644,0.00006622008,0.0001978768,0.000241248,0.00005775967],"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":[0.0002875821,0.0001710665,0.002296296,0.0002443274,0.00009042278,0.0002391649,0.0003259869,0.03003179,0.2282204,0.006085425,0.01781054,0.7141969],"study_design_scores_gemma":[0.00003725389,0.0001743011,0.005632932,0.00004072013,0.00006935011,0.0006769724,0.0002296018,0.7808517,0.1610296,0.01363752,0.0375193,0.0001007218],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01349547,0.0002468017,0.9738879,0.000194327,0.00004174402,0.0001116339,0.0003691233,0.009691723,0.001961302],"genre_scores_gemma":[0.1263728,0.0003306067,0.8650827,0.0002337889,0.00005270441,0.0001051055,0.002315703,0.001621431,0.003885094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007642326,"threshold_uncertainty_score":0.02556616,"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."}}