{"id":"W2771445442","doi":"10.1109/igarss.2017.8127667","title":"X-band interferometric sar observations for wetland water level monitoring in newfoundland and labrador","year":2017,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Interferometric synthetic aperture radar; Wetland; Remote sensing; Synthetic aperture radar; Peninsula; Water level; Interferometry; Vegetation (pathology); Landslide; Geology; Environmental science; Radar; Physical geography; Geography; Geomorphology; Cartography; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002245208,0.0002158206,0.0001279305,0.0006422257,0.0003578699,0.0004243234,0.0002872473,0.0001205173,0.0005617603],"category_scores_gemma":[0.0004087852,0.00009392531,0.0001248293,0.0006478377,0.0001947498,0.0002445541,0.0002181657,0.0001333517,0.0001220963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002019946,"about_ca_system_score_gemma":0.001965543,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8521416,"about_ca_topic_score_gemma":0.9623411,"domain_scores_codex":[0.9998502,0.0000190342,0.000007071006,0.00002516711,0.00003902477,0.00005962877],"domain_scores_gemma":[0.9997497,0.0000330081,0.00005731789,0.00001531783,0.0001061123,0.00003857342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000345819,0.0002107796,0.891037,0.00008692098,0.0001467561,0.0005281764,0.000516619,0.009554385,0.03037943,0.0001848008,0.003081336,0.06392797],"study_design_scores_gemma":[0.00001743271,0.00004709028,0.9895747,0.000006883684,0.00003447273,0.00004661212,0.000400056,0.006850139,0.001905331,0.000007646339,0.001100956,0.00000872873],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969397,0.0001627055,0.0005770495,0.00005116465,0.000003965092,0.00001896292,0.0006650222,0.00005049204,0.001531081],"genre_scores_gemma":[0.9948383,0.0001839896,0.002462477,0.00003719098,0.000003359404,0.00001343545,0.001187153,0.000008614021,0.001265508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1478584,"threshold_uncertainty_score":0.2974582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0585655109922317,"score_gpt":0.2744978196210504,"score_spread":0.2159323086288187,"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."}}