{"id":"W2129379831","doi":"10.1109/ccece.2004.1345219","title":"Lossy compression of DNA microarray images","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossy compression; Computer science; Quantization (signal processing); Data compression; Artificial intelligence; Entropy (arrow of time); Image compression; Computer vision; Discrete cosine transform; Transform coding; Entropy encoding; Lossless compression; Pattern recognition (psychology); Image processing; Image (mathematics); Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00001968757,0.00004656691,0.00005594013,0.00002340164,0.00002011309,0.000008858697,0.00007925347,0.00002407865,0.00003555623],"category_scores_gemma":[0.000001602983,0.0000400564,0.00001855932,0.00006518106,0.00002454545,0.00004797767,0.0000128839,0.00003937913,0.00001336521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001055701,"about_ca_system_score_gemma":0.000004783387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001153315,"about_ca_topic_score_gemma":5.98233e-7,"domain_scores_codex":[0.9997605,0.000001038375,0.00008537835,0.00005072739,0.0000360943,0.00006626426],"domain_scores_gemma":[0.9998368,0.000003744721,0.00001064166,0.0001124072,0.00002036741,0.00001603798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[4.725717e-7,0.00001710346,0.00001527929,0.00004294669,0.000002481876,2.667747e-7,0.00003179749,0.0008239117,0.9914076,0.00105904,0.00246652,0.004132518],"study_design_scores_gemma":[0.00007919512,0.000004249315,0.0001300959,0.000030954,0.000002351946,0.00000203067,0.000007647199,0.0003527882,0.9936294,0.00314888,0.002559483,0.00005293017],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06262708,0.0003223723,0.8974727,0.0002128501,0.00002918384,0.00009643546,0.000006130286,0.0008215907,0.03841164],"genre_scores_gemma":[0.8879283,0.0000258858,0.1118596,0.000018355,0.00001140729,0.000007852956,0.000002625074,0.000009425949,0.0001364945],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8253012,"threshold_uncertainty_score":0.1633453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007921553679220587,"score_gpt":0.2380643589531367,"score_spread":0.2301428052739161,"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."}}