{"id":"W4399352874","doi":"10.21428/d82e957c.a04caf7d","title":"Critical Infrastructure Asset Imaging Pipeline","year":2024,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Asset (computer security); Computer science; Pipeline (software); Identification (biology); Image quality; Computer vision; Artificial intelligence; Image resolution; Image (mathematics); Computer security","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":[0.0003203804,0.0009839493,0.0005534954,0.001768727,0.0003593628,0.001074544,0.00111661,0.0008007708,0.02594139],"category_scores_gemma":[0.0009670841,0.0004386302,0.0006468582,0.0008054881,0.0002038961,0.001274535,0.001598161,0.0008002613,0.01597426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005206694,"about_ca_system_score_gemma":0.0009948679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004288047,"about_ca_topic_score_gemma":0.006339633,"domain_scores_codex":[0.999716,0.00001565434,0.00001281828,0.00005548868,0.0001258244,0.00007408371],"domain_scores_gemma":[0.9996834,0.00003742714,0.00002143015,0.00006184933,0.000157183,0.00003870594],"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":[0.0005155475,0.0001487257,0.006050711,0.0007484854,0.0001170018,0.001132285,0.0003557629,0.01465194,0.09731388,0.00372315,0.1511016,0.7241409],"study_design_scores_gemma":[0.0001516119,0.0005236433,0.02908382,0.0002860943,0.0001737934,0.005293767,0.000673628,0.3706246,0.1573014,0.01331712,0.4223616,0.0002088325],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05049717,0.001837817,0.7948914,0.001336383,0.0003019876,0.001149898,0.01600312,0.0723488,0.06163345],"genre_scores_gemma":[0.3364917,0.001935153,0.5767679,0.001070384,0.000202223,0.0005851524,0.04461388,0.00375853,0.03457503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02594139,"threshold_uncertainty_score":0.08678263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005815915468202167,"score_gpt":0.2890365738648466,"score_spread":0.2832206583966445,"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."}}