{"id":"W4413052386","doi":"10.1109/tvcg.2025.3596541","title":"More Like Vis, Less Like Vis: Comparing Interactions for Integrating User Preferences Into Partial Specification Recommenders","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visualization; Computer science; Data visualization; Process (computing); Human–computer interaction; Interactive visualization; Comprehension; Creative visualization; Data mining; Information retrieval; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006195682,0.0009017998,0.0007031286,0.001050244,0.0004021872,0.002193163,0.0007046742,0.001172708,0.003414364],"category_scores_gemma":[0.06243725,0.000386316,0.0009432656,0.0007207374,0.0003711228,0.002676759,0.001282911,0.0009477888,0.0006929295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004478775,"about_ca_system_score_gemma":0.0004061063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002016195,"about_ca_topic_score_gemma":0.00360029,"domain_scores_codex":[0.9942613,0.00371061,0.0005403185,0.0006255811,0.0006726497,0.0001895621],"domain_scores_gemma":[0.9298815,0.06135385,0.002611885,0.003198474,0.002138127,0.0008162173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01876025,0.003139945,0.2501811,0.005097477,0.001936805,0.0004653775,0.01307612,0.01828666,0.05177062,0.002664055,0.006380992,0.6282406],"study_design_scores_gemma":[0.002146548,0.01897542,0.5973967,0.0009464726,0.002794485,0.001331803,0.009185591,0.3073434,0.03053829,0.009693139,0.0186133,0.001034859],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9448239,0.0005591525,0.0476425,0.0002127543,0.00003896179,0.0005093701,0.0009179068,0.00226942,0.00302599],"genre_scores_gemma":[0.9229242,0.0002005332,0.07399181,0.0001292398,0.00002322756,0.000503015,0.001139801,0.0001385481,0.0009497114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006195682,"threshold_uncertainty_score":0.03276628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05653016292367945,"score_gpt":0.3423513509854848,"score_spread":0.2858211880618053,"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."}}