{"id":"W2381241817","doi":"","title":"Application of Texture Mapping Technique in Virtual Campus Scene Based on VRML","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Simulation and Modeling Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Projective texture mapping; Computer science; Texture filtering; Texture mapping; VRML; Texture atlas; Texture (cosmology); Computer vision; Texture compression; Artificial intelligence; Bidirectional texture function; Computer graphics (images); Convolution (computer science); Pixel; Image texture; Virtual reality; Image (mathematics); Image processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002616627,0.0001662026,0.0001685821,0.0004324833,0.00006096147,0.00001324111,0.0002478819,0.0001482441,0.000006859855],"category_scores_gemma":[8.878841e-7,0.0001913459,0.00005870187,0.0007534604,0.00003535912,0.00004073614,0.00002185322,0.0002093191,0.00003969958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001172485,"about_ca_system_score_gemma":0.00002177803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000154059,"about_ca_topic_score_gemma":0.0000150672,"domain_scores_codex":[0.9989104,0.00001064505,0.0004553695,0.0002769716,0.0001261827,0.0002204129],"domain_scores_gemma":[0.9992368,0.0001133013,0.00007117091,0.0004326396,0.00007825212,0.00006784005],"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":[0.000006512182,0.0001745568,0.0009018974,0.00004788081,0.0000088774,3.174287e-7,0.0001677163,0.5944478,0.134101,0.003537039,0.0002437207,0.2663627],"study_design_scores_gemma":[0.0004564874,0.00001671604,0.005219575,0.00004261647,0.000006655134,0.000002993145,0.00003510269,0.8744858,0.03568228,0.0005003126,0.08328715,0.0002643293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005524659,0.00003595242,0.9910903,0.00007691554,0.00001192937,0.001361999,0.00001641874,0.0003455326,0.001536321],"genre_scores_gemma":[0.8642675,0.000003792086,0.1344815,0.0001673655,0.00007450504,0.000871162,0.00008773313,0.00003287343,0.00001358425],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8587428,"threshold_uncertainty_score":0.780286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005976915159369195,"score_gpt":0.2307353469182723,"score_spread":0.2247584317589031,"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."}}