{"id":"W2995446947","doi":"","title":"Designing an argumentative decision-aiding tool for urban planning. AIPA : an interface between multicriteria decision aiding and argumentative frameworks","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Argumentative; Argumentation theory; Computer science; Decision support system; Traceability; Management science; Process (computing); Operationalization; Decision engineering; Decision analysis; Relevance (law); Process management; Business decision mapping; Knowledge management; Artificial intelligence; Software engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01543058,0.001478412,0.001138981,0.003398222,0.002597935,0.01039402,0.003266233,0.004419087,0.02004978],"category_scores_gemma":[0.03072018,0.001388144,0.001954418,0.001747314,0.002971983,0.01071505,0.009353243,0.003580922,0.005760263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001586599,"about_ca_system_score_gemma":0.002270541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006049351,"about_ca_topic_score_gemma":0.0007221426,"domain_scores_codex":[0.9916,0.005525554,0.0007074426,0.0007253998,0.001168053,0.000273505],"domain_scores_gemma":[0.9765609,0.01964782,0.0007807223,0.001308715,0.001052168,0.0006497329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006341395,0.0007607936,0.002478238,0.003138331,0.0002452848,0.002127709,0.01689667,0.0464976,0.01877845,0.5536967,0.03242065,0.3223256],"study_design_scores_gemma":[0.0003448804,0.0002084768,0.0007074283,0.0009307234,0.0001315815,0.001091706,0.003034386,0.2631943,0.01617499,0.3279578,0.3860058,0.0002178631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004449602,0.0001480888,0.9818124,0.001007218,0.00009807005,0.0005046353,0.0004580441,0.004968083,0.006553832],"genre_scores_gemma":[0.04232056,0.0001446701,0.9509967,0.0002076985,0.00003425185,0.0008245505,0.0007244076,0.000671527,0.004075597],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02004978,"threshold_uncertainty_score":0.08160573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03530134999819801,"score_gpt":0.3166961891801536,"score_spread":0.2813948391819556,"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."}}