{"id":"W2953662108","doi":"10.22260/isarc2019/0060","title":"Text Detection and Classification of Construction Documents","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"BIM and Construction Integration","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Optical character recognition; Information retrieval; Set (abstract data type); Bounding overwatch; Task (project management); Minimum bounding box; Artificial intelligence; Class (philosophy); Character (mathematics); Document processing; Download; Deep learning; Document management system; Data set; Control (management); Natural language processing; Image (mathematics); World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005790617,0.0008436225,0.0006222515,0.006203923,0.000440955,0.001078274,0.000815886,0.0008827914,0.001683588],"category_scores_gemma":[0.002741594,0.0001601159,0.0006769132,0.00306765,0.0003177471,0.0008875115,0.0006012429,0.0005977084,0.002575688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007301265,"about_ca_system_score_gemma":0.0006563081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00531645,"about_ca_topic_score_gemma":0.005425948,"domain_scores_codex":[0.9988747,0.0001094518,0.0001336114,0.0002804489,0.0004669905,0.0001347811],"domain_scores_gemma":[0.9979787,0.0005848995,0.0003284928,0.0002127488,0.0007866432,0.0001084763],"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":[0.0006970973,0.00040857,0.02678534,0.0009198394,0.00008516471,0.0007183258,0.0003211573,0.007854677,0.04036494,0.000662526,0.02679934,0.894383],"study_design_scores_gemma":[0.0001213426,0.0006586679,0.1756923,0.000416174,0.0001876802,0.002646853,0.002003676,0.5192627,0.2229759,0.002525126,0.0733788,0.0001307541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8410732,0.00614418,0.09292684,0.0008793135,0.0004167854,0.000891416,0.03509804,0.01018849,0.01238175],"genre_scores_gemma":[0.7441829,0.001382934,0.1804401,0.0001120922,0.0001701853,0.0005174024,0.0623554,0.0002398886,0.01059909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006203923,"threshold_uncertainty_score":0.010571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005978358122068816,"score_gpt":0.1911489674612615,"score_spread":0.1851706093391927,"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."}}