{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002670341,0.0003479142,0.0002327052,0.0006520492,0.0003244399,0.0009218362,0.0004492893,0.0003164933,0.003176599],"category_scores_gemma":[0.001071456,0.0001973519,0.0003494984,0.0006102077,0.0004661141,0.0007406474,0.0006365569,0.0004405911,0.0004776022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002692003,"about_ca_system_score_gemma":0.0002633523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001934254,"about_ca_topic_score_gemma":0.001075901,"domain_scores_codex":[0.999662,0.00008355156,0.00001110772,0.00004347436,0.0001600641,0.00003982498],"domain_scores_gemma":[0.9997755,0.00007546455,0.00001391448,0.00005769295,0.00006227961,0.00001508671],"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":[0.0002707198,0.00006156356,0.001032313,0.0002359344,0.0000280492,0.0005555594,0.001179339,0.05105234,0.2624476,0.06579464,0.005989611,0.6113524],"study_design_scores_gemma":[0.0001207785,0.0003175419,0.004476032,0.0000710477,0.00009245799,0.002333828,0.0008601923,0.520873,0.3010854,0.02013802,0.149487,0.0001445666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02475432,0.0002058485,0.9552881,0.0002073209,0.00008852629,0.00003681555,0.00003393823,0.002055998,0.01732912],"genre_scores_gemma":[0.6161579,0.0006318756,0.3757594,0.00009561076,0.00007505548,0.00006608593,0.0001024707,0.0002695157,0.006841979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003176599,"threshold_uncertainty_score":0.01062679,"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."}}