{"id":"W1556610000","doi":"10.1007/11493402_14","title":"Intensional Programming for Agent Communication","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001076643,0.0003470911,0.0003635407,0.0004808628,0.000332719,0.0004410773,0.00239405,0.000236611,0.00001086874],"category_scores_gemma":[0.00008935092,0.0003143575,0.0001468975,0.000256396,0.0002432269,0.000546404,0.0008240402,0.0003905986,0.00003159814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003417973,"about_ca_system_score_gemma":0.0002673428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001995837,"about_ca_topic_score_gemma":0.0001041349,"domain_scores_codex":[0.9972826,0.00003132449,0.0005451941,0.0009936248,0.0006943425,0.0004529302],"domain_scores_gemma":[0.9974157,0.0004018479,0.0003743026,0.001289897,0.0003988906,0.0001194204],"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":[0.000003849609,0.00003003417,0.00001920861,0.00003654465,0.000008361652,0.000003797129,0.0006712757,0.007270819,0.00008493543,0.03253822,0.00008410553,0.9592488],"study_design_scores_gemma":[0.0004060039,0.0001330989,0.0001626513,0.0006034789,0.000008676626,0.0000405658,2.780525e-7,0.9201357,0.0005117352,0.02829766,0.0491084,0.0005917851],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003860172,0.000620978,0.9951396,0.001420434,0.001224873,0.0009058188,0.000003673816,0.0001338843,0.0005121261],"genre_scores_gemma":[0.08471587,0.00005058742,0.912787,0.001156569,0.0005664682,0.0000543914,0.00002543212,0.00002973628,0.0006139598],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9586571,"threshold_uncertainty_score":0.9999309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03600947419636275,"score_gpt":0.2776279431013793,"score_spread":0.2416184689050165,"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."}}