{"id":"W2126601384","doi":"10.1109/igarss.1999.772021","title":"Adaptive multiresolution quantization for contextual information gain in SAR sea ice images","year":2003,"lang":"en","type":"article","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Thresholding; Histogram; Computer science; Pixel; Synthetic aperture radar; Computer vision; Segmentation; Pattern recognition (psychology); Sea ice; Image segmentation; Quantization (signal processing); Multiresolution analysis; Geography; Wavelet; Wavelet transform; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006102235,0.0003933897,0.0004328103,0.0007527088,0.0002500132,0.0005991614,0.0004807129,0.0002670466,0.001143873],"category_scores_gemma":[0.002679584,0.0001898463,0.0004085585,0.000862607,0.0004003278,0.001088556,0.00061043,0.0005964726,0.0002586123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004760469,"about_ca_system_score_gemma":0.0003316496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001441211,"about_ca_topic_score_gemma":0.003159723,"domain_scores_codex":[0.9996215,0.00007902032,0.00002101728,0.00005369726,0.0001924121,0.00003236243],"domain_scores_gemma":[0.9994372,0.0002123765,0.00005212686,0.000140144,0.0001406453,0.00001752417],"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":[0.0003108227,0.00008008521,0.001146383,0.0001791036,0.00006235198,0.0001610902,0.0002815246,0.1370365,0.1446763,0.03037483,0.002765258,0.6829258],"study_design_scores_gemma":[0.00004635374,0.0001611846,0.002894059,0.00002913074,0.00005562247,0.0002221576,0.00007591561,0.8820152,0.08593284,0.01896742,0.009545564,0.00005457588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02461602,0.0004909015,0.973133,0.0001237141,0.00004724956,0.00003736016,0.00005165087,0.0005292394,0.0009708966],"genre_scores_gemma":[0.27354,0.0004517715,0.7245551,0.00006615747,0.00007306264,0.00005409573,0.0001530932,0.0001142788,0.0009924392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001441211,"threshold_uncertainty_score":0.003826618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01578103916740489,"score_gpt":0.2220679537482296,"score_spread":0.2062869145808247,"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."}}