{"id":"W2896092475","doi":"10.1109/jphot.2018.2876386","title":"Integration and Application of Microlens Arrays Within Heads-Up Displays","year":2018,"lang":"en","type":"article","venue":"IEEE photonics journal","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Mitacs; Innovation, Science and Economic Development Canada; Intel Corporation","keywords":"Microlens; Superlens; Curvature; Optics; Optical transfer function; Computer science; Materials science; Lens (geology); Substrate (aquarium); Optoelectronics; Computer graphics (images); Refractive index; Physics","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":[0.0001472891,0.00009584086,0.000122304,0.00007883852,0.00006356677,0.00002861309,0.0001310807,0.00006686582,0.000003301985],"category_scores_gemma":[0.00006934388,0.00008539313,0.00002418299,0.0001095815,0.0001751958,0.0001535976,0.00001538279,0.000312006,0.000007027907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005113328,"about_ca_system_score_gemma":0.00001078805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002455363,"about_ca_topic_score_gemma":0.00001420682,"domain_scores_codex":[0.9994384,0.000007241356,0.0002293527,0.00009361809,0.00009148334,0.0001398995],"domain_scores_gemma":[0.9995493,0.00002487347,0.0001282798,0.0001573063,0.00009607984,0.00004410545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001076745,0.000009172673,0.0001739831,0.00001483857,0.00001518846,9.749904e-7,0.0002878737,0.002240839,0.987193,0.0009783501,0.0002229108,0.008852064],"study_design_scores_gemma":[0.0001732124,0.00005267382,0.0001279138,0.00003822226,0.00001193861,0.0001248774,0.00009798424,0.2938098,0.694505,0.01053709,0.0004260238,0.00009518002],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4671575,0.0002121404,0.5319501,0.00005978546,0.0002903804,0.00005746219,0.000003244354,0.0001082319,0.0001611815],"genre_scores_gemma":[0.9126514,0.0002172135,0.08703116,0.00001662804,0.00005357453,0.000004788611,9.070353e-7,0.0000170843,0.000007193748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4454939,"threshold_uncertainty_score":0.3482231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008777347945872429,"score_gpt":0.2496176770861921,"score_spread":0.2408403291403197,"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."}}