{"id":"W4311387296","doi":"10.3390/s22249755","title":"An Adaptive Refinement Scheme for Depth Estimation Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Benchmark (surveying); Computer science; Inference; Generalization; Particle swarm optimization; Scheme (mathematics); Artificial intelligence; Segmentation; Feature (linguistics); Deep learning; Algorithm; Pattern recognition (psychology); Mathematics","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":[0.001072067,0.0009648495,0.0006089206,0.0006276596,0.0003612309,0.0003719249,0.002004496,0.0009274941,0.001872176],"category_scores_gemma":[0.003253918,0.0005231555,0.0007088952,0.0004638435,0.0006398009,0.001304492,0.001364689,0.001587991,0.000433721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008940971,"about_ca_system_score_gemma":0.0009071286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008959751,"about_ca_topic_score_gemma":0.01321387,"domain_scores_codex":[0.9994711,0.00009237541,0.00003573709,0.000166051,0.0001820221,0.00005268007],"domain_scores_gemma":[0.9991998,0.0002279902,0.0001061776,0.0001974421,0.0002296824,0.00003900694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002022877,0.00006397835,0.001272217,0.00009650497,0.00005240573,0.00009032474,0.0001694924,0.5677338,0.05116181,0.01303676,0.002397461,0.3637229],"study_design_scores_gemma":[0.00001114373,0.00004005394,0.0001295358,0.000004923166,0.000005918768,0.00002025529,0.000004535027,0.9916002,0.00564807,0.00173346,0.0007951554,0.000006784807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01148129,0.0001471852,0.9867594,0.0000751451,0.00002645548,0.00003976599,0.00004894399,0.0008586052,0.0005633288],"genre_scores_gemma":[0.3390035,0.0001631132,0.6575086,0.0001279201,0.0000366823,0.0001191741,0.0002187957,0.0001685642,0.002653729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008959751,"threshold_uncertainty_score":0.01781517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02395452314715214,"score_gpt":0.3012783051188913,"score_spread":0.2773237819717392,"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."}}