{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003338162,0.001275796,0.0007690606,0.0006773007,0.0004836236,0.002050522,0.002401906,0.00172299,0.01409825],"category_scores_gemma":[0.01488916,0.0007389938,0.001151239,0.000395783,0.000500062,0.005434521,0.003326866,0.002383317,0.004073587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005295047,"about_ca_system_score_gemma":0.0006897673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006426466,"about_ca_topic_score_gemma":0.000694291,"domain_scores_codex":[0.9972487,0.0008720087,0.0002699992,0.0004414921,0.0009557626,0.0002120113],"domain_scores_gemma":[0.993287,0.002150646,0.0002901009,0.00242825,0.001629049,0.0002148526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001104664,0.001070137,0.002983518,0.0008865917,0.0002388482,0.001078572,0.001671838,0.08350705,0.06881562,0.2890462,0.0228382,0.5267587],"study_design_scores_gemma":[0.0001512097,0.0002315195,0.0005771724,0.0001272837,0.0001770644,0.0008660955,0.0001777198,0.7310796,0.05777083,0.0983649,0.1104131,0.00006354939],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005257374,0.0000982085,0.9801577,0.0001702699,0.00007492657,0.0002254937,0.00007351528,0.004597294,0.009345271],"genre_scores_gemma":[0.1808613,0.0002024314,0.8039315,0.0003781924,0.00007501629,0.0005419846,0.0004136214,0.0009899277,0.01260603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01409825,"threshold_uncertainty_score":0.04716337,"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."}}