{"id":"W1919844839","doi":"10.1109/igarss.1999.775025","title":"Study of real-time lossless data compression for hyperspectral imagery","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Hyperspectral imaging; Lossless compression; Data compression; Computer science; Remote sensing; Artificial intelligence; Entropy (arrow of time); Entropy encoding; Encoder; JPEG 2000; Imaging spectrometer; Computer vision; Image compression; Spectrometer; Image processing; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008019617,0.0003228234,0.0003183763,0.0002603731,0.000138677,0.0002760955,0.0003996556,0.0002879273,0.0005313843],"category_scores_gemma":[0.00284679,0.0001153235,0.0001645764,0.0003517403,0.0005107304,0.001012869,0.0001652278,0.0004056983,0.00007375651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003226019,"about_ca_system_score_gemma":0.0001856699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006562753,"about_ca_topic_score_gemma":0.0003776547,"domain_scores_codex":[0.9996347,0.0001003542,0.00001274439,0.00003832647,0.000194745,0.00001908405],"domain_scores_gemma":[0.9974743,0.001972633,0.0001677807,0.000120635,0.0002376304,0.00002714555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001025408,0.0003166574,0.003026008,0.0004966071,0.00009748864,0.0004835055,0.0003405785,0.6308951,0.134286,0.007894299,0.0007943538,0.220344],"study_design_scores_gemma":[0.00001615759,0.0003487953,0.001091792,0.000009307983,0.00001102092,0.0001767299,0.00002734623,0.9442458,0.05303761,0.0004752743,0.0005513675,0.000008831164],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6736372,0.00178313,0.322145,0.0002532211,0.00005287866,0.00006772119,0.00005901039,0.0002530966,0.001748881],"genre_scores_gemma":[0.9439211,0.0008614187,0.05376858,0.00003363103,0.00004648133,0.00002834062,0.00008776839,0.00003914845,0.001213573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008019617,"threshold_uncertainty_score":0.004241228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06235719579032278,"score_gpt":0.3488913895200256,"score_spread":0.2865341937297028,"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."}}