{"id":"W4389539448","doi":"10.1145/3610548.3618144","title":"CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration","year":2023,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Universitas Brawijaya; Chinese University of Hong Kong","keywords":"Sketch; Leverage (statistics); Computer science; Active shape model; Space (punctuation); Artificial intelligence; Computer vision; Shape analysis (program analysis); ENCODE; Human–computer interaction; Algorithm; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000173761,0.0001141541,0.000156172,0.0001236885,0.00007810605,0.00007652427,0.0000554851,0.00005919278,0.00009921786],"category_scores_gemma":[0.00003038996,0.0001035058,0.00005437185,0.0002641853,0.00001278053,0.0001918847,0.00001777673,0.00005198092,0.0001036338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001087321,"about_ca_system_score_gemma":0.000005038063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007500993,"about_ca_topic_score_gemma":0.00002821632,"domain_scores_codex":[0.9993674,0.000005582839,0.0001624275,0.0001683776,0.00009732613,0.0001988462],"domain_scores_gemma":[0.9997098,0.00007307628,0.00001433334,0.0001088759,0.00003653024,0.00005742722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004554878,0.00004283997,0.001450961,0.0005184572,0.0004421789,0.000008320812,0.002386386,0.687784,0.01000582,0.001620881,0.02967693,0.2660177],"study_design_scores_gemma":[0.0002151838,0.00002010316,0.0000796972,0.00001575389,0.00003480338,3.209186e-7,0.0003350899,0.9970398,0.0001474869,0.0005092566,0.001462454,0.0001400848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7179262,0.0004621596,0.2768316,0.001078814,0.0002673634,0.0002577602,0.00001571421,0.002121512,0.001038857],"genre_scores_gemma":[0.9938561,0.0009246577,0.003931579,0.0000621566,0.0001514734,0.00007451019,0.00007736929,0.00003788164,0.0008843156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3092558,"threshold_uncertainty_score":0.4220844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04586022517929382,"score_gpt":0.258697129642063,"score_spread":0.2128369044627692,"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."}}