{"id":"W4413343973","doi":"10.1109/tmc.2025.3600434","title":"SC-GIR: Goal-Oriented Semantic Communication via Invariant Representation Learning for Image Transmission","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Invariant (physics); Theoretical computer science; Representation (politics); Artificial intelligence; Natural language processing; Multimedia; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000564107,0.0002467255,0.0002903264,0.0003892875,0.001060527,0.0001668022,0.00090961,0.0001179276,0.00001159634],"category_scores_gemma":[0.00003888645,0.0002565939,0.0001666123,0.0009373386,0.00007053663,0.0007947053,0.0000292379,0.0005132301,0.00001053073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001264227,"about_ca_system_score_gemma":0.00006785583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004934626,"about_ca_topic_score_gemma":0.000005030626,"domain_scores_codex":[0.9977726,0.0003070106,0.000618021,0.0006883624,0.0002715898,0.0003423631],"domain_scores_gemma":[0.997559,0.0007558171,0.0001966724,0.001082544,0.0003202699,0.00008566624],"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":[0.00005510181,0.0002483742,0.00001045142,0.00007764396,0.00003742562,0.00000301343,0.0005631275,0.1809276,0.06564278,0.001283197,0.0003413666,0.7508099],"study_design_scores_gemma":[0.0005915199,0.000124227,0.00004300122,0.0004125752,0.00002204189,0.000007810359,0.00006491021,0.7867558,0.2078302,0.001553591,0.002387822,0.0002064929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002084005,0.000118393,0.9945252,0.0004254206,0.0003510962,0.001172643,0.000004798771,0.001145116,0.0001733423],"genre_scores_gemma":[0.5831618,0.00007351561,0.4162106,0.0001375428,0.00001393325,0.0001890008,0.00001762193,0.00001855271,0.0001774088],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7506034,"threshold_uncertainty_score":0.9999886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01204250165624007,"score_gpt":0.3117689206239984,"score_spread":0.2997264189677583,"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."}}