{"id":"W4393206055","doi":"10.2139/ssrn.4772807","title":"Chatkg: Visualizing Time-Series Patterns Aided by Intelligent Agents and a Knowledge Graph","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Knowledge graph; Series (stratigraphy); Graph; Data science; Theoretical computer science; Artificial intelligence","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","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001907266,0.0005133043,0.0005900032,0.0004262114,0.0003530896,0.001224492,0.001225854,0.0002316723,0.00004859786],"category_scores_gemma":[0.00003075302,0.0004508623,0.000419362,0.000391403,0.00006829415,0.0003208729,0.002245858,0.00390306,0.0000925625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006208238,"about_ca_system_score_gemma":0.001287943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009955722,"about_ca_topic_score_gemma":0.0002746281,"domain_scores_codex":[0.995676,0.0001839732,0.000696084,0.0008406063,0.0003979989,0.002205376],"domain_scores_gemma":[0.9987192,0.00004646585,0.0004047194,0.0004874118,0.0001318269,0.0002104409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000907112,0.0004494848,0.002396304,0.0008661966,0.006751047,0.0001505668,0.00999086,0.0006611335,0.0006413059,0.2848759,0.00779818,0.6853283],"study_design_scores_gemma":[0.0006229635,0.0009799008,0.0001850671,0.001390772,0.0005715394,0.001824367,0.001832046,0.1090499,0.0005726384,0.8642613,0.01669442,0.002015174],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1105087,0.08337934,0.799522,0.002384081,0.001878713,0.0004619168,0.00005236941,0.0003991654,0.001413679],"genre_scores_gemma":[0.9659683,0.0255259,0.0008999914,0.0001220778,0.0006268707,0.00002382382,0.00003164343,0.0000961655,0.006705299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8554595,"threshold_uncertainty_score":0.9998123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321641418276782,"score_gpt":0.2647438250564592,"score_spread":0.2515274108736913,"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."}}