{"id":"W4416542124","doi":"10.1080/17538947.2025.2585752","title":"KDSR: knowledge-guided dynamic-static integrating scenario representation for urban physical examination","year":2025,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alpha Technologies (Canada)","funders":"National Natural Science Foundation of China","keywords":"Representation (politics); Visualization; Knowledge representation and reasoning; Graph; Scheduling (production processes); Data visualization; Urban computing; Urban planning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001872306,0.0001236932,0.0001833997,0.0003106472,0.00005631629,0.0004035359,0.001205247,0.00006139588,0.000001475568],"category_scores_gemma":[0.001324244,0.00011098,0.0001506091,0.0003073749,0.00006327834,0.001623976,0.0002582888,0.0002352646,0.000005156412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001371417,"about_ca_system_score_gemma":0.0001708553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001262931,"about_ca_topic_score_gemma":0.000005181437,"domain_scores_codex":[0.9988062,0.0000258433,0.0004687554,0.000208676,0.000339654,0.0001508691],"domain_scores_gemma":[0.997892,0.0004669838,0.0003753613,0.0002214939,0.001009214,0.00003499134],"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":[0.00002606839,0.0002411128,0.0008555535,0.00001481585,0.0001777792,0.00002152468,0.0009913504,0.001257616,0.0008048932,0.1886884,0.00109205,0.8058289],"study_design_scores_gemma":[0.002534992,0.0006515326,0.005099265,0.0004657782,0.00004434649,0.0001903303,0.0006025769,0.6151,0.01271535,0.3513515,0.01083983,0.0004045543],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04672677,0.00006913246,0.9438597,0.001945536,0.001274962,0.0001440785,0.000007678846,0.00008184217,0.005890319],"genre_scores_gemma":[0.9535414,0.000008133951,0.04526138,0.00007239648,0.0001276489,0.00000787608,0.000008520836,0.000007045351,0.0009655762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9068146,"threshold_uncertainty_score":0.4525634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01798500458182366,"score_gpt":0.3443016077910653,"score_spread":0.3263166032092416,"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."}}