{"id":"W1914990675","doi":"10.1109/dcc.2006.4","title":"A Unified Framework for Lossless Image Set Compression","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Lethbridge","funders":"","keywords":"Lossless compression; Image compression; Computer science; Entropy encoding; Spanning tree; Redundancy (engineering); Centroid; Image (mathematics); Data compression; Lossy compression; Minimum spanning tree; Entropy (arrow of time); Algorithm; ENCODE; Scheme (mathematics); Graph; Artificial intelligence; Mathematics; Theoretical computer science; Image processing; Discrete mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001380577,0.0001608483,0.0001786214,0.00008740149,0.0001583308,0.0001671569,0.00126265,0.0001020204,0.00003598718],"category_scores_gemma":[0.0000498479,0.0001295807,0.00006401494,0.0002479468,0.00004997272,0.00076656,0.0005528339,0.0001410955,0.00002846479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002520038,"about_ca_system_score_gemma":0.00002289383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003183075,"about_ca_topic_score_gemma":0.000002938072,"domain_scores_codex":[0.9987299,0.00003834886,0.0002526006,0.0004583618,0.000217977,0.000302849],"domain_scores_gemma":[0.9983587,0.0003329357,0.0001094645,0.001020868,0.0001119221,0.00006614548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001086642,0.00006070397,0.00003080028,0.00001588382,0.000002250114,0.000005088368,0.00001980595,0.00003171324,0.01111521,0.9214697,0.05622813,0.01100979],"study_design_scores_gemma":[0.0002534261,0.00004486933,0.0002228521,0.00007126085,0.000002225874,0.000005431932,0.000008134247,0.02749968,0.1747048,0.7520447,0.04489398,0.0002486361],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003904383,0.00004703491,0.9941885,0.0006322225,0.0001642588,0.0003702998,0.00003048069,0.00118722,0.002989469],"genre_scores_gemma":[0.06151816,0.000004212029,0.937337,0.000350729,0.00008460391,0.00007996963,0.00003754819,0.00001428461,0.0005734551],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1694251,"threshold_uncertainty_score":0.528415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242962522075121,"score_gpt":0.3223165000410087,"score_spread":0.2998868748202575,"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."}}