{"id":"W1598212980","doi":"10.1109/igarss.1999.774616","title":"Assessment of speckle reduction filters for automatic detection of small/thin features [in SAR images]","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Speckle pattern; Artificial intelligence; Feature (linguistics); Filter (signal processing); Computer science; Computer vision; Speckle noise; Reduction (mathematics); Feature extraction; Pattern recognition (psychology); Contrast (vision); Synthetic aperture radar; Mathematics","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":[0.000992623,0.00008105695,0.0001765038,0.0001705738,0.00003291083,0.00003109277,0.0001907202,0.00004463704,0.00001158415],"category_scores_gemma":[0.000110893,0.00007185742,0.00007491412,0.000298867,0.0000278451,0.0002097243,0.00002210864,0.0000713753,5.196749e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004673197,"about_ca_system_score_gemma":0.00005906801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007164539,"about_ca_topic_score_gemma":0.000007751581,"domain_scores_codex":[0.9990602,0.0001870681,0.0002779058,0.0001951709,0.0001431828,0.0001364463],"domain_scores_gemma":[0.9993781,0.0001230952,0.0001306885,0.0002609569,0.00008393981,0.00002322672],"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":[0.00000952067,0.0001370022,0.00009108902,0.0001491574,0.0000178685,0.000002157993,0.0003448935,0.001277419,0.9044703,0.01323888,0.0002530885,0.08000866],"study_design_scores_gemma":[0.000532602,0.0001860382,0.0117097,0.00003552159,0.000008342309,0.00002052842,0.00007138669,0.05771605,0.9229454,0.006623033,0.00005844882,0.00009299383],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04322501,0.00004305597,0.9532326,0.00006085563,0.000328101,0.0002204117,7.322397e-7,0.00003459188,0.002854651],"genre_scores_gemma":[0.4672906,0.000002081141,0.5322993,0.000013311,0.000008539651,0.000004587055,3.441522e-7,0.000003348582,0.000377787],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4240656,"threshold_uncertainty_score":0.2930261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02151808334674407,"score_gpt":0.3059240651294006,"score_spread":0.2844059817826565,"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."}}