{"id":"W2905140526","doi":"10.1109/crv.2018.00014","title":"Deep Autoencoders with Aggregated Residual Transformations for Urban Reconstruction from Remote Sensing Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Computer science; Artificial intelligence; Remote sensing; Pattern recognition (psychology); Geology; Algorithm","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":[0.0007255151,0.001071161,0.000577163,0.0006116451,0.0002093688,0.0006004966,0.001123496,0.0007113608,0.001430417],"category_scores_gemma":[0.001490591,0.0004713673,0.0007542198,0.0006234623,0.0004410994,0.001119158,0.0008050047,0.00143931,0.0007842193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006277268,"about_ca_system_score_gemma":0.0007202266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009917433,"about_ca_topic_score_gemma":0.01229584,"domain_scores_codex":[0.9997448,0.00005539035,0.00001270407,0.00008800346,0.00005733161,0.00004163591],"domain_scores_gemma":[0.9996067,0.0001543324,0.00004677722,0.0000853653,0.00009050504,0.00001627885],"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":[0.000109442,0.00008189984,0.00111372,0.00006158967,0.00008823728,0.00007335337,0.00006669333,0.7642173,0.007361179,0.003772661,0.001904311,0.2211496],"study_design_scores_gemma":[0.000001853327,0.00000822385,0.0001464944,0.000003357607,0.000004985121,0.000005992698,0.00000608628,0.9972556,0.001135883,0.001174974,0.0002541025,0.00000250412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03869976,0.0004262778,0.9564941,0.0001598348,0.00004247748,0.00003147895,0.0002230061,0.002580427,0.00134268],"genre_scores_gemma":[0.6250783,0.0005367103,0.3665114,0.0002097048,0.00006993896,0.0001162474,0.002173352,0.0002760361,0.005028424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009917433,"threshold_uncertainty_score":0.01971942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0214035520957816,"score_gpt":0.2435863994030679,"score_spread":0.2221828473072863,"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."}}