{"id":"W6930534962","doi":"10.5281/zenodo.14066108","title":"TracerX-WP: Pruning Dynamic Symbolic Execution with Weakest Precondition Interpolation","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Precondition; Interpolation (computer graphics); Pruning; Measure (data warehouse); Process (computing); Class (philosophy)","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":[0.001657846,0.002562369,0.001541626,0.001945954,0.001253014,0.002084201,0.003037503,0.001546467,0.04506861],"category_scores_gemma":[0.009404852,0.001199625,0.001705586,0.001829093,0.002379498,0.002622401,0.004024222,0.002885712,0.00744992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001269146,"about_ca_system_score_gemma":0.004430037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01131071,"about_ca_topic_score_gemma":0.0192822,"domain_scores_codex":[0.9978067,0.0005168258,0.0001356178,0.0003467204,0.0008790723,0.0003150804],"domain_scores_gemma":[0.9961036,0.002060733,0.0001612967,0.001021737,0.0004939242,0.00015868],"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":[0.002401571,0.0003612194,0.002514614,0.001441205,0.0002587631,0.0004761226,0.0005174238,0.2698444,0.01354758,0.0971757,0.08489561,0.5265658],"study_design_scores_gemma":[0.0003540014,0.0001021325,0.0002004597,0.0001177132,0.00006611652,0.00006262078,0.00006499155,0.8884691,0.01872496,0.0696715,0.02212248,0.00004389394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01338565,0.0004198335,0.905882,0.0003985892,0.0002922976,0.0001818167,0.001661036,0.06195871,0.01582004],"genre_scores_gemma":[0.2163939,0.0002734011,0.7423272,0.0003598555,0.0001089954,0.0005884497,0.004575205,0.01753397,0.01783903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04506861,"threshold_uncertainty_score":0.1507695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00846240755453186,"score_gpt":0.2239395998699216,"score_spread":0.2154771923153897,"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."}}