{"id":"W2070639429","doi":"10.1145/2366145.2366205","title":"The magic lens","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Lens (geology); Computer science; MAGIC (telescope); Animation; Computer vision; Computer graphics (images); Decoding methods; Artificial intelligence; Optics; ENCODE; Grid; Gradient-index optics; Physics; Algorithm; Mathematics; Refractive index; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002756263,0.0008302523,0.0004832646,0.0008109065,0.0005960694,0.00146112,0.001445922,0.0008817809,0.008208241],"category_scores_gemma":[0.0008598538,0.0005474208,0.0005325926,0.00054822,0.000942298,0.001931735,0.001212776,0.0008988626,0.002642513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004794179,"about_ca_system_score_gemma":0.0006592369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004346282,"about_ca_topic_score_gemma":0.0005608212,"domain_scores_codex":[0.9994509,0.00004734786,0.00003243571,0.0001347915,0.0002914022,0.00004312355],"domain_scores_gemma":[0.9995677,0.00006503652,0.00006445451,0.0001575257,0.0001012784,0.00004402951],"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":[0.0002337596,0.0000827987,0.001075717,0.0006994338,0.00006773719,0.0004948856,0.0003078488,0.0189442,0.2694117,0.3454745,0.009810627,0.3533968],"study_design_scores_gemma":[0.0001630671,0.0009578688,0.0007810007,0.0001740863,0.0001248418,0.00374278,0.0001098908,0.2283239,0.333129,0.06947682,0.3628077,0.0002089523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0169353,0.001762279,0.9375744,0.0003960446,0.0004431813,0.0001519838,0.0001879532,0.002026265,0.04052253],"genre_scores_gemma":[0.1776244,0.001065413,0.8048873,0.0002163321,0.0001069505,0.0001525544,0.000169393,0.0001869617,0.01559073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008208241,"threshold_uncertainty_score":0.02745932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02762942484397209,"score_gpt":0.2633473573686147,"score_spread":0.2357179325246426,"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."}}