{"id":"W4404586293","doi":"10.1145/3699730","title":"SynthCAT: Synthesizing Cellular Association Traces with Fusion of Model-Based and Data-Driven Approaches","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Data association; Association (psychology); Fusion; Computer science; Sensor fusion; Artificial intelligence; Psychology","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":[0.0003264533,0.0001411256,0.0002180688,0.0001974535,0.0001039655,0.0002026659,0.001642946,0.00009518654,8.057011e-7],"category_scores_gemma":[0.0007843273,0.00008939089,0.00002919666,0.0003855717,0.0001449106,0.0007658937,0.001380113,0.0001983013,5.258997e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004501559,"about_ca_system_score_gemma":0.0000415968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008740487,"about_ca_topic_score_gemma":0.00000204966,"domain_scores_codex":[0.9989622,0.000009568464,0.0001996622,0.000433891,0.0002509304,0.0001437267],"domain_scores_gemma":[0.9988206,0.0002714188,0.0002515509,0.0005225413,0.0001155414,0.0000183291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003757791,0.001224138,0.0116383,0.00519372,0.0009607357,0.000008928354,0.007426157,0.01091296,0.4515848,0.1305579,0.004766467,0.3753501],"study_design_scores_gemma":[0.00007889967,0.0001962014,0.00003485257,0.0007295684,0.00004264739,0.00000285402,0.001805678,0.5629233,0.4299125,0.003932063,0.0002329077,0.000108561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734797,0.001532264,0.01906411,0.004030589,0.00008859127,0.0005352481,0.00006402194,0.0005954774,0.0006099804],"genre_scores_gemma":[0.9922855,0.0002882278,0.007289933,0.00001953063,0.000006872625,0.00003021954,0.000002013914,0.00001008824,0.00006755986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5520104,"threshold_uncertainty_score":0.3645255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03872138332647597,"score_gpt":0.2798678863130781,"score_spread":0.2411465029866022,"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."}}