{"id":"W1984260661","doi":"10.1093/iwc/iwt060","title":"Natural Language-based Representation of User Preferences","year":2013,"lang":"en","type":"article","venue":"Interacting with Computers","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Preference; Representation (politics); Human–computer interaction; Natural language; Artificial intelligence; Exploratory research; Natural (archaeology); Basis (linear algebra); Machine learning","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":[0.004712226,0.0007275077,0.0004738773,0.001369569,0.0005434,0.002296121,0.001259213,0.0009718483,0.003894223],"category_scores_gemma":[0.01850889,0.0004091318,0.001060791,0.001728496,0.0007753951,0.005503942,0.001140772,0.001523931,0.0007875917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067871,"about_ca_system_score_gemma":0.001006204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002130104,"about_ca_topic_score_gemma":0.00387464,"domain_scores_codex":[0.9940954,0.003734788,0.0004778342,0.0005467136,0.0009406996,0.0002045215],"domain_scores_gemma":[0.9881052,0.007181271,0.0008115115,0.001726357,0.001997969,0.0001777521],"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":[0.000910126,0.0005194337,0.007537051,0.001342078,0.0002395608,0.0007403116,0.009115028,0.07367472,0.02738073,0.655154,0.01016103,0.2132259],"study_design_scores_gemma":[0.0001639985,0.0003790043,0.003165125,0.0003128493,0.0002069216,0.0008528772,0.00221553,0.591378,0.01614624,0.3377348,0.04725384,0.0001906837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0440269,0.000310652,0.9427782,0.001149765,0.00004162996,0.0003326884,0.001408786,0.001181411,0.008769868],"genre_scores_gemma":[0.5110987,0.0003229282,0.4827397,0.0003085034,0.00003110821,0.0008288836,0.001856369,0.0001460624,0.0026678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004712226,"threshold_uncertainty_score":0.02492094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001196278948445,"score_gpt":0.2529376165729137,"score_spread":0.2429256537834293,"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."}}