{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000154066,0.00006390914,0.00006381681,0.00004642597,0.0002769206,0.00003652378,0.0002582316,0.000008945002,0.0000147896],"category_scores_gemma":[0.00001861063,0.0000652924,0.00002872025,0.000171798,0.00001014265,0.0001972518,0.0001111369,0.00008260801,0.000003891042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005209191,"about_ca_system_score_gemma":0.00001589486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004743018,"about_ca_topic_score_gemma":0.000002064551,"domain_scores_codex":[0.9993172,0.00003864964,0.0001038642,0.0002299368,0.0001458749,0.0001644326],"domain_scores_gemma":[0.9995554,0.0000334555,0.0000546614,0.0002609932,0.0000469563,0.00004850125],"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":[0.00001553064,0.00003221228,0.00002454126,0.000001272557,0.000003516928,0.000002397318,0.0002239708,0.7576606,0.00009616278,0.01335311,0.0008433259,0.2277433],"study_design_scores_gemma":[0.0002049316,0.0001830815,0.0001479763,0.000002504195,0.000001319382,0.000004098413,0.0001496813,0.9914716,0.0001626904,0.001397386,0.006187145,0.00008759917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007545758,0.00003045028,0.9909952,0.0005330402,0.0002278861,0.0001557945,0.000001739645,0.0001197948,0.0003903424],"genre_scores_gemma":[0.465949,0.000001229702,0.5331996,0.0006420199,0.00002867333,0.00003046785,0.000007564728,0.000006033413,0.0001354098],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4584032,"threshold_uncertainty_score":0.2662547,"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."}}