{"id":"W2998693894","doi":"10.1609/aaai.v34i04.5918","title":"Differentiable Algorithm for Marginalising Changepoints","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Differentiable function; Algorithm; Inference; Series (stratigraphy); Computer science; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003170719,0.0002564712,0.000318107,0.00007340188,0.0002374445,0.0003654129,0.002174378,0.00009787132,0.00004065484],"category_scores_gemma":[0.0002495792,0.0001978997,0.0001654041,0.0005753711,0.0001503759,0.0004117586,0.0003913006,0.0002662643,0.00005523496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002766175,"about_ca_system_score_gemma":0.00008338498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000160385,"about_ca_topic_score_gemma":0.000001221597,"domain_scores_codex":[0.9980079,0.00001427173,0.0004932929,0.0006175735,0.0004320063,0.0004349404],"domain_scores_gemma":[0.9985111,0.00007936669,0.000322497,0.000247647,0.0006659424,0.0001734021],"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":[0.00002950149,0.00007853923,0.0000192409,0.00006074029,0.00001670209,2.951535e-7,0.001129919,0.00002412859,0.01774929,0.7194268,0.0005390808,0.2609258],"study_design_scores_gemma":[0.0000324176,0.0002135269,0.00001455848,0.0001288058,0.00001193312,0.000002012034,0.0001593016,0.5664351,0.2954955,0.137137,0.0001733768,0.0001964407],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00933352,0.0000288532,0.9739968,0.01356923,0.0002920277,0.0004730323,0.00001322577,0.0001481234,0.002145235],"genre_scores_gemma":[0.9412356,0.00001824473,0.05733185,0.001001902,0.0001609065,0.00004960788,9.257612e-7,0.00001767841,0.000183261],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9319021,"threshold_uncertainty_score":0.8070114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1269404337266036,"score_gpt":0.2970119024827202,"score_spread":0.1700714687561166,"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."}}