{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006394936,0.00174899,0.001016995,0.0009170693,0.0004635706,0.001696632,0.003253645,0.001591757,0.01090563],"category_scores_gemma":[0.002764607,0.0008369854,0.001743325,0.0008163237,0.001076541,0.002918781,0.004656319,0.002412695,0.002915034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006471516,"about_ca_system_score_gemma":0.0009027116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002631123,"about_ca_topic_score_gemma":0.006457448,"domain_scores_codex":[0.9995465,0.00009016456,0.00001468628,0.0001287598,0.000172803,0.00004716733],"domain_scores_gemma":[0.9992658,0.0003197546,0.00003719989,0.000225956,0.00006969196,0.000081572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005527259,0.0003585923,0.001898419,0.0007539091,0.0002085621,0.0005162029,0.0006287965,0.3550121,0.05052884,0.03935937,0.03374586,0.5164367],"study_design_scores_gemma":[0.00004413195,0.0001052713,0.0002395189,0.00003245582,0.00001471902,0.0002116922,0.00006319229,0.9552834,0.01101278,0.01814817,0.01480138,0.00004337426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008410275,0.0003859427,0.9769177,0.0001816982,0.00005838216,0.0001095428,0.0005478992,0.01007011,0.003318399],"genre_scores_gemma":[0.1779984,0.0006428109,0.8068498,0.000604719,0.00006558408,0.0005162668,0.002407265,0.002948011,0.007967073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01090563,"threshold_uncertainty_score":0.03648293,"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."}}