{"id":"W4405563750","doi":"10.1109/tcsvt.2024.3519723","title":"An Image Terrain Map Model for Texture Filtering","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Image texture; Terrain; Image segmentation; Image (mathematics); Texture (cosmology); Texture compression; Computer graphics (images); Pattern recognition (psychology); Geography; Cartography","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":[0.0001623616,0.0001434243,0.0001621728,0.0001494653,0.0002992317,0.0001014725,0.0001394077,0.000177443,0.00001218727],"category_scores_gemma":[0.000002863292,0.0001296451,0.00007091211,0.0001720894,0.0001368685,0.0001403801,0.000001434383,0.0001522915,0.0000287023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000745807,"about_ca_system_score_gemma":0.0000120449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006769639,"about_ca_topic_score_gemma":0.000079686,"domain_scores_codex":[0.9990147,0.00001151722,0.0001998096,0.0004465199,0.00008119328,0.0002462156],"domain_scores_gemma":[0.999521,0.00005835731,0.00002929486,0.0003153835,0.00001240977,0.00006352308],"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":[0.00001448239,0.0001209757,0.000004578469,0.0003455989,0.0000644645,0.000004130154,0.0009542926,0.04848823,0.5066817,0.002634314,0.003545777,0.4371414],"study_design_scores_gemma":[0.0001780753,0.0001466605,0.00000402607,0.00006188269,0.00003487881,0.00006084864,0.0002176496,0.967796,0.005017192,0.00246099,0.02384578,0.000176051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01570938,0.0001253215,0.9814836,0.0007391319,0.0003331688,0.000803082,0.0001799813,0.0004035457,0.0002228067],"genre_scores_gemma":[0.9965317,0.00001302091,0.001947158,0.000046063,0.00003559872,0.000233325,0.000004546119,0.00003495044,0.001153671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9808223,"threshold_uncertainty_score":0.5286774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695532564700178,"score_gpt":0.2635159422099828,"score_spread":0.246560616562981,"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."}}