{"id":"W2090532686","doi":"10.1080/13658810412331280185","title":"A method for examining the spatial dimension of multi-criteria weight sensitivity","year":2004,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiple-criteria decision analysis; Attractiveness; Dimension (graph theory); Stakeholder; Computer science; Robustness (evolution); Geographic information system; Spatial analysis; Sensitivity (control systems); Data mining; Management science; Operations research; Geography; Mathematics; Statistics; Cartography; Engineering","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":[0.002162377,0.00008351442,0.0002525734,0.0002884632,0.00005196809,0.00007190435,0.0001657891,0.0000715025,0.00002079845],"category_scores_gemma":[0.0001221256,0.0000685116,0.000160922,0.00006826036,0.00004975665,0.00076361,0.00003574491,0.0001027115,0.00001989536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009829293,"about_ca_system_score_gemma":0.00001665339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003945161,"about_ca_topic_score_gemma":0.000009079226,"domain_scores_codex":[0.9984739,0.00003629366,0.001213684,0.00007273282,0.000111035,0.00009236896],"domain_scores_gemma":[0.9981263,0.0001423802,0.001415304,0.00008963012,0.0001857223,0.00004070249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0009003985,0.0008078821,0.3430214,0.0002269199,0.00221712,0.000009176889,0.009453771,0.1921473,0.003549414,0.4118793,0.0007747735,0.03501254],"study_design_scores_gemma":[0.01117234,0.0007337766,0.6541576,0.0003668205,0.00007189664,0.0004719238,0.001946485,0.2841271,0.002021772,0.01093571,0.03343407,0.0005605477],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2841657,0.000143506,0.7132608,0.0005562255,0.001376372,0.0001836819,0.0001062435,0.000004419338,0.0002031092],"genre_scores_gemma":[0.9912944,0.00005644919,0.008259136,0.0001604073,0.0001833809,0.000008767978,0.00002500185,0.00000526632,0.000007210409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7071287,"threshold_uncertainty_score":0.2793822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07271125303533933,"score_gpt":0.2755889497739145,"score_spread":0.2028776967385751,"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."}}