{"id":"W2026614901","doi":"10.1109/igarss.2014.6946638","title":"SAR for surface water monitoring and public health","year":2014,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; University of Calgary","funders":"","keywords":"Vegetation (pathology); Remote sensing; Interferometric synthetic aperture radar; Environmental science; Coherence (philosophical gambling strategy); Wetland; Radar; SIGNAL (programming language); Synthetic aperture radar; Backscatter (email); Amplitude; Interferometry; Water level; Phase (matter); Radar imaging; Geology; Optics; Physics; Geography; Computer science; Telecommunications","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":[0.0008486159,0.000580215,0.0004215733,0.0008317993,0.0002183888,0.0008762731,0.0004020749,0.000938788,0.0214024],"category_scores_gemma":[0.001130444,0.0001356118,0.0002432349,0.00115427,0.0003131826,0.0006657966,0.0006812944,0.0007366672,0.009647105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003819071,"about_ca_system_score_gemma":0.0005149539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006767171,"about_ca_topic_score_gemma":0.0005781424,"domain_scores_codex":[0.9995816,0.0001651149,0.00001823803,0.00006241408,0.0001457255,0.00002688352],"domain_scores_gemma":[0.999364,0.0001844345,0.00005591171,0.000112778,0.0002467987,0.00003609573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003205024,0.00009771638,0.002407859,0.0007201788,0.00006794939,0.000226444,0.00008322674,0.005022806,0.03102463,0.03133254,0.1302678,0.7984284],"study_design_scores_gemma":[0.00009543246,0.0004426881,0.007987499,0.0004272007,0.0000820858,0.0008778686,0.0002109027,0.0372623,0.02236499,0.03467428,0.8955191,0.00005562424],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0304311,0.08136985,0.4321718,0.02317901,0.005233014,0.000576639,0.01072504,0.007299623,0.4090138],"genre_scores_gemma":[0.470708,0.05722142,0.2585217,0.005757722,0.00340031,0.0007398428,0.01203553,0.0007643514,0.1908511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0214024,"threshold_uncertainty_score":0.07159811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01851438020003997,"score_gpt":0.2470092900921876,"score_spread":0.2284949098921476,"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."}}