{"id":"W1891915826","doi":"10.1109/dcc.2004.1281514","title":"Lossless and lossy compression of DNA microarray images","year":2004,"lang":"en","type":"article","venue":"","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Lossy compression; Lossless compression; Compression (physics); Microarray; Computer science; Data compression; DNA microarray; DNA; Materials science; Artificial intelligence; Gene; Genetics; Biology; Composite material; Gene expression","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":[0.000061229,0.0000781885,0.0001072887,0.00003046629,0.00002230854,0.00001053082,0.00009021561,0.00006578355,0.00002018917],"category_scores_gemma":[0.00001300402,0.00006513493,0.00004841599,0.00004036136,0.0001126058,0.000002396714,0.0001004191,0.00003009084,0.000001781054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003564607,"about_ca_system_score_gemma":0.00001383006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006426799,"about_ca_topic_score_gemma":0.00001863769,"domain_scores_codex":[0.9995375,0.00001310011,0.0001229036,0.0001857959,0.00005397688,0.00008673617],"domain_scores_gemma":[0.9996254,0.000002943201,0.00004666197,0.0002371654,0.00005539307,0.00003240901],"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":[0.00001101991,0.0000411039,0.0009496449,0.00001282141,0.00001856898,0.000001492527,0.000009918331,0.000003355658,0.9962918,0.00002819309,0.001572077,0.001060012],"study_design_scores_gemma":[0.0002453433,0.00006328688,0.00108305,0.00001236046,0.0000141949,0.000007608982,0.00002420374,0.000001843748,0.9957818,0.0001716604,0.002513722,0.00008087247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812817,0.0007075988,0.01245218,0.00008067006,0.000005150105,0.00006582325,0.000002390676,0.00001473408,0.005389735],"genre_scores_gemma":[0.9927101,0.0003108399,0.006147512,0.0001047714,0.00002489457,0.000002486193,0.00002615391,0.000008393974,0.0006648855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01142834,"threshold_uncertainty_score":0.2656125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004546179075668949,"score_gpt":0.247970381857032,"score_spread":0.2434242027813631,"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."}}