{"id":"W4407247436","doi":"10.1109/tgrs.2025.3539532","title":"New Wideband Large Aperture Open-Ended Coaxial Microwave Probe for Soil Dielectric Characterization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Université de Sherbrooke; Center for Northern Studies; AXYS Technologies (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Wideband; Dielectric; Microwave; Characterization (materials science); Microwave imaging; Aperture (computer memory); Remote sensing; Coaxial; Materials science; Synthetic aperture radar; Optics; Optoelectronics; Geology; Acoustics; Computer science; Telecommunications; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002345142,0.0002174515,0.0002214927,0.0001292678,0.0009650615,0.0002795551,0.0001766472,0.0001551387,0.000005539212],"category_scores_gemma":[0.00002438891,0.0001826732,0.00007713577,0.000728927,0.0001378678,0.0004342805,0.00001188815,0.00021995,0.00000980876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001018424,"about_ca_system_score_gemma":0.00008698638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001017104,"about_ca_topic_score_gemma":0.002896883,"domain_scores_codex":[0.9984865,0.00004733717,0.0002353453,0.0005927693,0.0001956466,0.0004424551],"domain_scores_gemma":[0.9994211,0.0001250479,0.00007977527,0.0002259276,0.00002432455,0.0001237771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006780206,0.00002515873,0.000004024314,0.000009158139,0.000008561846,0.000002164977,0.0002120576,0.00003320386,0.3432786,0.000009292989,0.0001283022,0.6562217],"study_design_scores_gemma":[0.004868856,0.0005052321,0.009221875,0.0006028516,0.0002567798,0.0002192209,0.0004141926,0.4137189,0.5196698,0.00302175,0.04619844,0.001302119],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02708943,0.00002147186,0.9666083,0.001641446,0.0007563645,0.0006376333,0.000005464614,0.00005343724,0.003186474],"genre_scores_gemma":[0.9207845,0.0001477415,0.05967574,0.003995562,0.00009129396,3.751344e-7,0.00001259502,0.00002648031,0.01526575],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9069325,"threshold_uncertainty_score":0.7449197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009169490627128372,"score_gpt":0.2373511612423487,"score_spread":0.2281816706152203,"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."}}