{"id":"W1519528253","doi":"10.1007/11422778_11","title":"An Energy-Efficient Image Representation for Secure Mobile Systems","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Encryption; Image compression; Energy (signal processing); Energy consumption; Transmission (telecommunications); Image quality; Image file formats; JPEG; Computation; Representation (politics); Data compression; Theoretical computer science; Image (mathematics); Algorithm; Real-time computing; Image processing; Computer vision; Computer network; Mathematics; Telecommunications","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"],"consensus_categories":[],"category_scores_codex":[0.000729917,0.000596449,0.0005982316,0.0008714059,0.0003391298,0.0008962621,0.004682824,0.0003577913,0.00001101629],"category_scores_gemma":[0.00006122281,0.0005490821,0.0001445049,0.000542341,0.0004507103,0.001389003,0.001212271,0.0004856137,0.00001048916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003899955,"about_ca_system_score_gemma":0.0003221932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002732696,"about_ca_topic_score_gemma":0.00001746011,"domain_scores_codex":[0.9951299,0.00006163751,0.000714482,0.002303979,0.001083742,0.0007062554],"domain_scores_gemma":[0.9953341,0.0004813747,0.0005135834,0.002911053,0.0005180837,0.0002418059],"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":[0.000009652234,0.0000714467,0.000001645237,0.00004086634,0.000006120621,0.00002395035,0.0003166179,0.3751878,0.002210289,0.05792425,0.0001977035,0.5640096],"study_design_scores_gemma":[0.0002113008,0.0002692706,0.000002927875,0.0002735239,0.000005920071,0.00004831939,4.103577e-7,0.9293816,0.01134687,0.04553379,0.01229413,0.000631901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001671121,0.0007161266,0.9951665,0.0001232533,0.001460371,0.00109924,0.00005508753,0.0006454894,0.0007172188],"genre_scores_gemma":[0.03626349,0.00006516025,0.9615803,0.0005064866,0.0008861728,0.0002640684,0.00006949148,0.00006593732,0.0002988515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5633777,"threshold_uncertainty_score":0.9996961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446164982040928,"score_gpt":0.2966752493291403,"score_spread":0.282213599508731,"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."}}