{"id":"W2030921890","doi":"10.1145/1160633.1160635","title":"Agent interface enhancement","year":2006,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Interfacing; Computer science; Interface (matter); Multi-agent system; Probabilistic logic; Task (project management); Set (abstract data type); Bayesian network; Graphical model; Graphical user interface; Human–computer interaction; Distributed computing; Artificial intelligence; Programming language; Engineering; Systems engineering; Parallel computing; Computer hardware","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007839644,0.00006008974,0.00005081925,0.00002239174,0.00003558865,0.00008959655,0.0003613633,0.00001808759,0.0000802658],"category_scores_gemma":[0.0000016771,0.00004936979,0.00002136157,0.00008774838,0.00001019608,0.0001275265,0.0001073196,0.00004180093,0.0003948967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001901273,"about_ca_system_score_gemma":0.00001646912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009711974,"about_ca_topic_score_gemma":0.000009194934,"domain_scores_codex":[0.9994355,0.00001154281,0.0001106213,0.0001833824,0.0001135999,0.0001453539],"domain_scores_gemma":[0.9996651,0.000008733427,0.00001909311,0.0002497225,0.00002713902,0.00003021586],"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":[0.000001475007,0.0001285178,0.00008626124,0.000005322224,0.000005590007,0.000005703093,0.0001186733,0.000895424,0.01653142,0.8852726,0.0269055,0.07004349],"study_design_scores_gemma":[0.0003085217,0.0001619574,0.0006665649,0.00003004801,0.000003848756,0.00001205742,0.00001614699,0.5175524,0.3547188,0.08810431,0.03795737,0.0004679855],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006829998,0.00007894492,0.9364991,0.0007317548,0.0001636123,0.00003060326,9.024379e-8,0.0001360214,0.05552984],"genre_scores_gemma":[0.914526,0.000003531672,0.07821856,0.0002529345,0.00002755515,0.000004564187,3.451502e-7,0.000001933591,0.006964528],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9076961,"threshold_uncertainty_score":0.5075729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859913675469622,"score_gpt":0.2574504424337129,"score_spread":0.2388513056790167,"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."}}