{"id":"W4400423765","doi":"10.3390/land13071008","title":"Comparison of the Analytic Network Process and the Best–Worst Method in Ranking Urban Resilience and Regeneration Prioritization by Applying Geographic Information Systems","year":2024,"lang":"en","type":"article","venue":"Land","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Athabasca University","funders":"","keywords":"Resilience (materials science); Multiple-criteria decision analysis; Geographic information system; Urbanization; Analytic network process; Ranking (information retrieval); Sustainability; Sustainable development; Process (computing); Environmental resource management; Metropolitan area; Computer science; Per capita; Prioritization; Geography; Analytic hierarchy process; Business; Operations research; Environmental science; Mathematics; Population; Economics; Ecology; Economic growth; Process management; Cartography","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":[0.0006274071,0.00005492414,0.0001096699,0.0000210192,0.0001304726,0.0001323452,0.00007314244,0.0000332166,0.00000203042],"category_scores_gemma":[0.000008562697,0.00002847982,0.00001053601,0.0003476651,0.00002618774,0.0003431646,0.00003735541,0.00005934139,0.000001094035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001270828,"about_ca_system_score_gemma":0.000003602629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001634483,"about_ca_topic_score_gemma":0.002285953,"domain_scores_codex":[0.9993728,0.00009057651,0.000207613,0.00009702359,0.0001472139,0.0000847794],"domain_scores_gemma":[0.9997459,0.00007843161,0.00007894009,0.00007807378,0.000005154142,0.00001351585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009823792,0.000002959813,0.9448654,0.0001805134,0.000004405284,6.605296e-8,0.001477682,0.05216284,0.00003285945,0.0001109756,0.00003213955,0.001120307],"study_design_scores_gemma":[0.0002927421,0.0000116219,0.05236148,0.0003177931,0.00003090449,0.000003657549,0.0004294481,0.9456539,0.00006386216,0.0002376769,0.0005353733,0.00006149059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940379,0.003079375,0.002008845,0.000101337,0.00008534099,0.0004078631,0.000002263909,0.000009234832,0.0002678154],"genre_scores_gemma":[0.9997646,0.0001104867,0.00003501424,0.00001863749,0.00002602375,0.00002935264,0.000005759168,0.000002439273,0.000007693649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8934911,"threshold_uncertainty_score":0.2470861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006190937758038838,"score_gpt":0.2500653665493704,"score_spread":0.2438744287913316,"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."}}