{"id":"W4402602691","doi":"10.1007/978-3-031-61499-6_13","title":"Exploring the Feasibility of Deep Learning-Based Boundary Extraction for Scan-To-BIM: A Case Study Analysis","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Boundary (topology); Extraction (chemistry); Artificial intelligence; Deep learning; Computer science; Mathematics; Chromatography; Chemistry","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":[0.002645172,0.0007325006,0.0004669122,0.001017641,0.0005680686,0.002241816,0.001383113,0.001637371,0.004212286],"category_scores_gemma":[0.006175415,0.0004092695,0.0006008561,0.00143058,0.0007899905,0.00289208,0.001399804,0.001039842,0.0009292039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046066,"about_ca_system_score_gemma":0.001420467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009680003,"about_ca_topic_score_gemma":0.01529562,"domain_scores_codex":[0.998985,0.0003629469,0.00003967348,0.0001731393,0.0003132799,0.0001259741],"domain_scores_gemma":[0.9968424,0.00213319,0.00009972539,0.0002981643,0.0005626486,0.00006385828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008573542,0.0007481989,0.01282906,0.0006331577,0.0001061886,0.0008588407,0.0008041882,0.316717,0.03829008,0.01686899,0.006465474,0.6048215],"study_design_scores_gemma":[0.00001965023,0.0001126794,0.001780705,0.00005100188,0.00003028625,0.0001941251,0.0005583407,0.9684377,0.0166179,0.00752825,0.004653683,0.00001569669],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3579307,0.0009352624,0.6198666,0.001411774,0.00008480719,0.000296107,0.0008211055,0.001748247,0.01690534],"genre_scores_gemma":[0.7444577,0.0002588494,0.2512584,0.00009878339,0.00001183055,0.00007032704,0.0005875279,0.0001753901,0.00308119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009680003,"threshold_uncertainty_score":0.01924729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06155320853554476,"score_gpt":0.2671856408905167,"score_spread":0.205632432354972,"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."}}