{"id":"W4405390300","doi":"10.1111/mice.13397","title":"A semi‐supervised approach for building wall layout segmentation based on transformers and limited data","year":2024,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Segmentation; Computer science; Floor plan; Artificial intelligence; Transformer; Regularization (linguistics); Automation; Consistency (knowledge bases); Pattern recognition (psychology); Engineering drawing; Engineering","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":[0.001004695,0.001226652,0.001487409,0.001557039,0.0005825701,0.001135663,0.002821331,0.001192466,0.002173712],"category_scores_gemma":[0.001865548,0.0009019781,0.001456487,0.001711224,0.001006729,0.002152523,0.001615174,0.001325659,0.001507225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007122009,"about_ca_system_score_gemma":0.001735611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004649879,"about_ca_topic_score_gemma":0.01076537,"domain_scores_codex":[0.9989105,0.0001871038,0.00005316721,0.0004322997,0.0002743496,0.0001426249],"domain_scores_gemma":[0.9987459,0.0003919335,0.0001497386,0.0003509405,0.0002896473,0.00007170695],"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":[0.0004985753,0.0003708132,0.002503193,0.0002355257,0.000130569,0.0001963997,0.0002511923,0.1965315,0.03862362,0.00819763,0.007674728,0.7447863],"study_design_scores_gemma":[0.00001055676,0.00004727236,0.0004095894,0.000006620837,0.00001206882,0.00005180907,0.0000297475,0.9898981,0.005583144,0.002878267,0.001064812,0.000007974298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02891065,0.0001519107,0.9656705,0.00008397173,0.00003168756,0.00006978451,0.0001774766,0.003705272,0.001198674],"genre_scores_gemma":[0.4380601,0.0002006327,0.55163,0.0002432119,0.00008302027,0.0002214406,0.003146258,0.0007154,0.005699941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004649879,"threshold_uncertainty_score":0.009245634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0167588373294902,"score_gpt":0.2099454491519316,"score_spread":0.1931866118224413,"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."}}