{"id":"W3156965179","doi":"10.48550/arxiv.2106.10800","title":"Lossy Compression for Lossless Prediction","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Computer science; Lossless compression; Lossy compression; Data compression; Fidelity; Gas compressor; JPEG; Artificial intelligence; Data compression ratio; Image compression; Set (abstract data type); Lossless JPEG; Data mining; Machine learning; Pattern recognition (psychology); Image (mathematics); Image processing","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":[0.0001967105,0.0000925767,0.0001179541,0.00006566953,0.0002000994,0.00008381683,0.0004156805,0.0000658658,0.00001704127],"category_scores_gemma":[0.00004171235,0.0001018976,0.00009448634,0.0004560164,0.00003633412,0.0005070005,0.0001824875,0.00008651461,0.00002376218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004491887,"about_ca_system_score_gemma":0.00007119671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006755361,"about_ca_topic_score_gemma":0.000002407398,"domain_scores_codex":[0.999092,0.0001232111,0.00009148783,0.0004428676,0.00005376237,0.0001966002],"domain_scores_gemma":[0.9990938,0.0001440147,0.00005040247,0.0004345277,0.0001967417,0.00008050293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002548126,0.0004929514,0.004926623,0.0001563276,0.0001309196,0.001615753,0.0006389663,0.053073,0.05101496,0.8472431,0.008058074,0.03239448],"study_design_scores_gemma":[0.002044656,0.0001003627,0.002322619,0.00006387041,0.00003977346,0.00003421907,0.00006277776,0.8870746,0.04315731,0.05473649,0.01008344,0.0002798701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06028825,0.0000525417,0.9371071,0.0000900798,0.0004095194,0.00008217163,0.000006226775,0.0001217131,0.00184236],"genre_scores_gemma":[0.9714977,0.00002309886,0.02394338,0.0001655717,0.00007397799,4.178403e-7,0.00000811751,0.00000690767,0.004280818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9131638,"threshold_uncertainty_score":0.4155264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0854099425829707,"score_gpt":0.2103444191192601,"score_spread":0.1249344765362894,"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."}}