{"id":"W4400055411","doi":"10.1007/s10032-024-00487-6","title":"Automatic floor plan analysis using a boundary attention-based deep network","year":2024,"lang":"en","type":"article","venue":"International Journal on Document Analysis and Recognition (IJDAR)","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Plan (archaeology); Artificial intelligence; Boundary (topology); Pattern recognition (psychology); Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008469895,0.0001968656,0.0003157975,0.001194191,0.0003820131,0.001636914,0.0001894615,0.00007216789,0.01400893],"category_scores_gemma":[0.0000265413,0.000142134,0.0006077333,0.001653314,0.00005342186,0.0004098315,0.000009952185,0.0002707685,0.0001990853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004238965,"about_ca_system_score_gemma":0.00005721517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000299815,"about_ca_topic_score_gemma":0.001162605,"domain_scores_codex":[0.9979444,0.0002198139,0.0005203752,0.0003209511,0.0007440701,0.0002504606],"domain_scores_gemma":[0.9991979,0.0001664767,0.0001862944,0.00008876774,0.000176244,0.0001843347],"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":[0.0001362748,0.00007516941,0.3836281,0.00003126276,0.02132849,0.0004852249,0.0002567505,0.1524423,0.00002406646,0.00002677165,0.0003668616,0.4411987],"study_design_scores_gemma":[0.0002969835,0.00008856038,0.1922296,0.0001614906,0.003828579,0.00005858543,0.0001440352,0.8004053,0.000007961597,0.001436429,0.00106888,0.0002735615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875001,0.001295604,0.008718793,0.0004845165,0.001115093,0.00006978551,0.0001360175,0.00006658845,0.0006134597],"genre_scores_gemma":[0.9956482,0.000227402,0.001559997,0.0003951632,0.0005774613,0.000001326948,0.001299496,0.000005396584,0.0002855372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.647963,"threshold_uncertainty_score":0.9993995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212746797109204,"score_gpt":0.264864999975496,"score_spread":0.2435903202645756,"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."}}