{"id":"W2205720127","doi":"10.1080/15599612.2015.1059536","title":"Optimal Spatial Resolution of Omnidirectional Imaging Systems for Pipe Inspection Applications","year":2015,"lang":"en","type":"article","venue":"International Journal of Optomechatronics","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Omnidirectional antenna; Resolution (logic); Image resolution; Telecommunications","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":[0.0004816958,0.000398669,0.0003162146,0.0002776455,0.0001789865,0.000644565,0.0003570968,0.0004495324,0.0007109962],"category_scores_gemma":[0.001607635,0.0003526177,0.00026996,0.0002878733,0.0003931753,0.0006975586,0.0004538621,0.0003468571,0.0002190942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006108356,"about_ca_system_score_gemma":0.0006549572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009330804,"about_ca_topic_score_gemma":0.00105717,"domain_scores_codex":[0.9995673,0.00007195678,0.00001997137,0.00006724441,0.0002331675,0.00004025665],"domain_scores_gemma":[0.9993212,0.0002892679,0.0001534846,0.00004538858,0.0001681488,0.00002256497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001911034,0.0001137477,0.003688693,0.0007920658,0.00004933934,0.0005095112,0.0003886912,0.4339131,0.4225256,0.04213513,0.002047272,0.09364577],"study_design_scores_gemma":[0.00002160103,0.0001757826,0.002371535,0.00005827014,0.0000254533,0.0007563439,0.0001511211,0.9063872,0.0790468,0.006931534,0.004012192,0.00006220893],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05123796,0.001693055,0.9392554,0.0001672386,0.00001833707,0.00004072923,0.00005845767,0.0001434288,0.007385426],"genre_scores_gemma":[0.7047081,0.001988403,0.2914517,0.00006723575,0.00002377179,0.00006730234,0.00006601318,0.00005815396,0.001569337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009330804,"threshold_uncertainty_score":0.004431903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03550047879153135,"score_gpt":0.3156061459510428,"score_spread":0.2801056671595115,"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."}}