{"id":"W2107241286","doi":"10.1007/11795018_25","title":"Representing and Querying Line Graphs in Natural Language: The iGraph System","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Natural language; Architecture; Natural (archaeology); Graph; Interface (matter); User interface; Human–computer interaction; Artificial intelligence; Natural language processing; World Wide Web; Theoretical 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":[0.001513973,0.0004174814,0.0004809865,0.0009151095,0.000270334,0.0007454478,0.002220046,0.0002240796,9.069375e-7],"category_scores_gemma":[0.000102389,0.0002978449,0.0001158073,0.0009668647,0.000470504,0.0004212113,0.001043503,0.0008852486,0.000007289929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466419,"about_ca_system_score_gemma":0.000167797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009815325,"about_ca_topic_score_gemma":0.001065387,"domain_scores_codex":[0.9966394,0.00008350471,0.0005785675,0.001283487,0.000770409,0.0006446637],"domain_scores_gemma":[0.9976643,0.0006192734,0.0002934894,0.001222879,0.0001099258,0.00009016484],"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":[0.00002618343,0.00005258589,0.004704698,0.0005929504,0.00004215078,0.001703343,0.007126812,0.02925433,0.001206747,0.07796817,0.0002273571,0.8770947],"study_design_scores_gemma":[0.0007384096,0.00009904624,0.002748468,0.002111087,0.00001278186,0.0006679918,0.000009169215,0.9598402,0.001260475,0.03098491,0.0003517997,0.001175657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003560805,0.007765123,0.97987,0.0004991281,0.00298464,0.0006365982,0.000003781652,0.0002394158,0.004440508],"genre_scores_gemma":[0.9705483,0.00001420544,0.02843626,0.0003092876,0.0004571859,0.00001050127,0.000004516035,0.00002146435,0.0001982872],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9669875,"threshold_uncertainty_score":0.9999474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009458823394421336,"score_gpt":0.2287483582566528,"score_spread":0.2192895348622315,"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."}}