{"id":"W7077872568","doi":"10.5281/zenodo.16946459","title":"Evaluating Low-Dimensional Latent Representations as a Creative Interface for Digital Synthesizers","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Generative grammar; Set (abstract data type); Workflow; Context (archaeology); Key (lock); Matching (statistics); Hidden Markov model; Interface (matter)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007110396,0.0009133597,0.0005329514,0.0007951693,0.0003273105,0.003075015,0.001454141,0.001355037,0.004736893],"category_scores_gemma":[0.04876223,0.0003579349,0.0008484296,0.0005109507,0.000837222,0.002819101,0.003016873,0.0009844811,0.0008614574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004638945,"about_ca_system_score_gemma":0.0003586007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006300053,"about_ca_topic_score_gemma":0.0009833243,"domain_scores_codex":[0.9960076,0.002674539,0.000179368,0.0004862917,0.0005348851,0.0001172861],"domain_scores_gemma":[0.9547675,0.04100994,0.0009988138,0.002140957,0.000631202,0.0004515876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.006704999,0.002608981,0.02800644,0.00284693,0.0003509989,0.0007250514,0.02612267,0.07642789,0.117828,0.01861973,0.004925734,0.7148325],"study_design_scores_gemma":[0.001261601,0.0042971,0.02040317,0.0004141843,0.0003070263,0.001167011,0.007013827,0.8745297,0.04599123,0.02703783,0.0172405,0.0003368387],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5461354,0.0004203204,0.4445952,0.0003224376,0.00004919046,0.0004090167,0.0004878881,0.003708682,0.003871905],"genre_scores_gemma":[0.7582065,0.000124429,0.2388072,0.00009150863,0.00002113903,0.0004149807,0.0005984382,0.0003226942,0.001413037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007110396,"threshold_uncertainty_score":0.03760386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04687215466420002,"score_gpt":0.3119505062104739,"score_spread":0.2650783515462739,"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."}}