{"id":"W4414105009","doi":"10.1016/j.geomat.2025.100071","title":"Multi-agent systems of large language models as weight assigners: An approach to collaborative weighting in spatial multi-criteria decision-making","year":2025,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shahid Beheshti University","keywords":"Weighting; Multiple-criteria decision analysis; Robustness (evolution); Group decision-making; Process (computing); Outlier; Parsing; Obstacle","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007524139,0.0005443534,0.001387887,0.002140651,0.0003144844,0.0009366455,0.002140039,0.0003136539,0.0003096853],"category_scores_gemma":[0.01038607,0.0004360575,0.0001931252,0.003618672,0.0001058138,0.001016465,0.001031119,0.0003257806,0.0002190032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002498376,"about_ca_system_score_gemma":0.0003416386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003916942,"about_ca_topic_score_gemma":0.0004463612,"domain_scores_codex":[0.9906197,0.001469553,0.003050441,0.001543739,0.002410729,0.000905828],"domain_scores_gemma":[0.9922923,0.003609343,0.0007697775,0.001970085,0.00104693,0.0003114927],"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":[0.003276363,0.01555147,0.01973053,0.001195439,0.0005531079,0.000921445,0.2495909,0.2269765,0.09562848,0.04105854,0.01100345,0.3345137],"study_design_scores_gemma":[0.00230674,0.00007757013,0.00505833,0.001129891,0.00002789298,0.000009408192,0.02368546,0.9639022,0.0004908309,0.002337275,0.0004986828,0.0004757728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3619488,0.0003189326,0.6327039,0.00005227227,0.0008993071,0.001262078,0.0001506924,0.00007394691,0.002590042],"genre_scores_gemma":[0.7132977,0.000002978857,0.285944,0.0002021809,0.00006796859,0.0001330636,0.00001172251,0.00003629083,0.0003041285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7369256,"threshold_uncertainty_score":0.9998091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07395771069311624,"score_gpt":0.4278588283182398,"score_spread":0.3539011176251236,"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."}}