{"id":"W4409606439","doi":"10.3390/electronics14081671","title":"Depth Upsampling with Local and Nonlocal Models Using Adaptive Bandwidth","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Upsampling; Bandwidth (computing); Computer science; Geology; Telecommunications; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005252831,0.0008808614,0.0006210867,0.000862148,0.000230681,0.0006065044,0.001014223,0.0006802525,0.001094333],"category_scores_gemma":[0.001755869,0.0004424867,0.0008252092,0.000659356,0.000409552,0.001513385,0.001388829,0.001069226,0.0004330077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004875703,"about_ca_system_score_gemma":0.0007769468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004084229,"about_ca_topic_score_gemma":0.006225398,"domain_scores_codex":[0.9995475,0.00007528174,0.00001889988,0.00007294985,0.0002494781,0.00003575762],"domain_scores_gemma":[0.9995539,0.0001549264,0.00006723889,0.00009485251,0.0001006864,0.00002840605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003765666,0.0001235438,0.001786942,0.0003515102,0.0001626647,0.000293925,0.0004899525,0.316216,0.1503674,0.01759559,0.003310176,0.5089257],"study_design_scores_gemma":[0.00001264276,0.00003810227,0.0002927289,0.00001572984,0.00002634763,0.0001495178,0.00003214696,0.9806051,0.01312708,0.00336287,0.002317682,0.00002005562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01151503,0.0003223643,0.9870734,0.00008129934,0.00001790603,0.00002434028,0.00003344741,0.0003424067,0.0005898149],"genre_scores_gemma":[0.2664886,0.001022966,0.7290933,0.0002005854,0.00006743948,0.0001068565,0.0003001717,0.0002041854,0.002515827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004084229,"threshold_uncertainty_score":0.008120894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668713078471975,"score_gpt":0.2297973672099578,"score_spread":0.2131102364252381,"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."}}