{"id":"W3210938546","doi":"10.3390/app11136188","title":"Automated Extraction and Time-Cost Prediction of Contractual Reporting Requirements in Construction Using Natural Language Processing and Simulation","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Software Engineering Research","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workflow; Computer science; Overhead (engineering); Identification (biology); Database; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0008084559,0.00005756775,0.00009821274,0.0001147445,0.0001146016,0.0001359655,0.00006795493,0.00003498261,0.000001038666],"category_scores_gemma":[0.0006341409,0.00005669197,0.000006519849,0.0004868845,0.0001152687,0.0006440028,0.00006103848,0.000083955,1.764509e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003849376,"about_ca_system_score_gemma":0.00008130351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002515654,"about_ca_topic_score_gemma":0.000002245346,"domain_scores_codex":[0.9989711,0.00002216128,0.0003182761,0.0002701098,0.000280668,0.0001376685],"domain_scores_gemma":[0.9993765,0.0002085757,0.0002485227,0.0000764203,0.00006406783,0.0000258708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009401176,0.00003034991,0.03588116,0.00008052544,0.000006683015,0.00001957421,0.002275965,0.05257503,0.7492028,0.0002991512,0.000002293233,0.159617],"study_design_scores_gemma":[0.0001528821,0.000008953837,0.04762675,0.00004209872,0.00000201296,0.0000499583,0.0002361136,0.9332598,0.01853403,0.00003924512,0.00000184075,0.00004632701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9578044,0.0002488286,0.04153556,0.0000123458,0.00007885406,0.0001202483,6.262161e-7,0.0001300169,0.00006908532],"genre_scores_gemma":[0.9666665,0.000002628728,0.03330238,0.000004260126,0.00001392114,0.00000317727,0.000001769812,0.000002414122,0.000002916687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8806847,"threshold_uncertainty_score":0.2311831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03723358363328755,"score_gpt":0.3453840418439745,"score_spread":0.3081504582106869,"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."}}