{"id":"W2136612652","doi":"10.1109/3dim.2007.6","title":"A Sampling Criterion for Optimizing a Surface Light Field","year":2007,"lang":"en","type":"article","venue":"Proceedings","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Sampling (signal processing); Field (mathematics); Surface (topology); Mathematics; Computer vision; Geometry","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":[0.000398377,0.00009864705,0.0001039128,0.00006771765,0.0001652078,0.00020726,0.000358168,0.00003707469,0.000003344273],"category_scores_gemma":[0.0001583236,0.00009391512,0.0000496179,0.0002342844,0.000007288911,0.0008172949,0.000134481,0.00009504292,0.000006637667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002680805,"about_ca_system_score_gemma":0.00001238656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002447209,"about_ca_topic_score_gemma":2.358547e-7,"domain_scores_codex":[0.9990694,8.962792e-7,0.0001746054,0.0002985897,0.0001239381,0.0003325828],"domain_scores_gemma":[0.9995146,0.0000922607,0.00006576229,0.00008802397,0.0001557132,0.00008357374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007479155,0.00006671018,0.0007137164,0.0001667628,0.00001264883,0.000004319116,0.006163022,0.000050074,0.456549,0.04660286,0.005353164,0.4842429],"study_design_scores_gemma":[0.0009408506,0.0002970266,0.0002688433,0.0002931861,0.000008859928,0.00005593796,0.0007274,0.3148489,0.4898717,0.01320922,0.1788377,0.0006403268],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007207651,0.0001211561,0.9852294,0.002240681,0.0002205164,0.0001528853,1.695928e-7,0.0002199681,0.004607518],"genre_scores_gemma":[0.2040808,0.000007557692,0.7944349,0.001204045,0.00008285542,0.000004169526,2.095396e-7,0.000009124547,0.0001763675],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4836026,"threshold_uncertainty_score":0.3829747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02807062206024899,"score_gpt":0.3275494188111202,"score_spread":0.2994787967508712,"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."}}