{"id":"W2580134368","doi":"10.1007/s13319-017-0115-1","title":"Introducing a Public Stereoscopic 3D High Dynamic Range (SHDR) Video Database","year":2017,"lang":"en","type":"preprint","venue":"3D Research","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital; University of British Columbia","funders":"","keywords":"Stereoscopy; Standardization; Computer science; High dynamic range; Process (computing); Impression; Range (aeronautics); Multimedia; Database; Computer graphics (images); Computer vision; Dynamic range; World Wide Web; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.004283295,0.0004152215,0.0005907116,0.00115596,0.001108406,0.003077328,0.007160234,0.000233543,0.0002021931],"category_scores_gemma":[0.00216072,0.0003822965,0.0001203383,0.00051622,0.0003823571,0.001628215,0.01903333,0.003590057,0.0006865488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004756621,"about_ca_system_score_gemma":0.001111406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005470348,"about_ca_topic_score_gemma":0.0001222991,"domain_scores_codex":[0.9931287,0.0007851805,0.0005170432,0.002179249,0.001924637,0.001465154],"domain_scores_gemma":[0.991091,0.0004511489,0.000269345,0.006915147,0.0008219834,0.0004513962],"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":[0.00002930824,0.0002166035,0.00056814,0.0007525581,0.00007560322,0.0003863231,0.0008842269,0.000130684,0.001461118,0.00364256,0.01085162,0.9810013],"study_design_scores_gemma":[0.001818972,0.0001747241,0.003120898,0.002223742,0.00001517844,0.0000507181,0.00008815493,0.8763154,0.000485871,0.01609495,0.09828815,0.001323293],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00546938,0.002458604,0.9662951,0.01757319,0.002975741,0.001147958,0.00007107457,0.0004128977,0.003596118],"genre_scores_gemma":[0.348386,0.001702024,0.6392263,0.0004253314,0.001098319,0.0003345076,0.0002159779,0.0001250763,0.008486532],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.979678,"threshold_uncertainty_score":0.9998629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116795051944835,"score_gpt":0.4308385017412291,"score_spread":0.3191589965467456,"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."}}