{"id":"W4366547603","doi":"10.1145/3544548.3580876","title":"Tesseract: Querying Spatial Design Recordings by Manipulating Worlds in Miniature","year":2023,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada); University of Toronto","funders":"","keywords":"Computer science; Workflow; Human–computer interaction; Interface design; Information retrieval; Database","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":[0.000293176,0.0001323705,0.0001378484,0.0002130187,0.0000932305,0.0001368798,0.0004330305,0.00006043454,0.00008209866],"category_scores_gemma":[0.0000987057,0.0001232608,0.00004892288,0.0007889497,0.000008790824,0.0007688088,0.0001730401,0.0002187738,0.0003066577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005755142,"about_ca_system_score_gemma":0.00002706901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005168086,"about_ca_topic_score_gemma":0.00005686255,"domain_scores_codex":[0.998854,0.00006535986,0.0002025242,0.0003566298,0.0001621909,0.0003592583],"domain_scores_gemma":[0.9993139,0.0003080768,0.00007121865,0.0002061594,0.00005366049,0.00004699006],"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":[0.00007453621,0.0001570383,0.04177327,0.00004390219,0.00005150332,0.0002784196,0.005297794,0.0008834496,0.6572458,0.008229697,0.240438,0.04552655],"study_design_scores_gemma":[0.001142357,0.0002389949,0.1232022,0.000306363,0.000009637685,0.00002349961,0.001485092,0.6161853,0.24523,0.00182788,0.009211094,0.001137555],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1273739,0.00003402092,0.8524496,0.002258413,0.001011962,0.0002926064,0.00000208007,0.0001634887,0.01641395],"genre_scores_gemma":[0.9869403,0.000005999947,0.008911284,0.0008561444,0.00005537784,0.00001650657,0.000009810289,0.00001238496,0.00319221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8595664,"threshold_uncertainty_score":0.5026432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0269821355865625,"score_gpt":0.2732778809082052,"score_spread":0.2462957453216427,"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."}}