{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"edceb9847ff4","filters":{"venue":"EUSAR 2014; 10th European Conference on Synthetic Aperture Radar; Proceedings of"}},"results":[{"id":"W371451046","doi":"","title":"Soil moisture retrieval using L-band time-series SAR data from the SMAPVEX12 experiment","year":2014,"lang":"en","type":"article","venue":"EUSAR 2014; 10th European Conference on Synthetic Aperture Radar; Proceedings of","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Synthetic aperture radar; Remote sensing; Water content; Environmental science; Radar; Vegetation (pathology); Inversion (geology); Moisture; Soil science; Meteorology; Geology; Geography; Computer science","authors":[{"name":"Seung Bum Kim","is_ca":false},{"name":"Huan Huang","is_ca":false},{"name":"Leung Tsang","is_ca":false},{"name":"Thomas J. Jackson","is_ca":false},{"name":"Heather McNairn","is_ca":true},{"name":"Jakob van Zyl","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02893530953440176,"gpt":0.2349377518090051,"spread":0.2060024422746033,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001015485,0.0004810384,0.0004814008,0.00003946586,0.0003654024,0.0001838943,0.001574975,0.0001462233,0.0006948979],"category_scores_gemma":[0.0006551246,0.0003108877,0.0001073548,0.0001897273,0.0007378539,0.0003345049,0.0006863268,0.0004673539,0.001116136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000832693,"about_ca_system_score_gemma":0.00003332656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003022969,"about_ca_topic_score_gemma":0.00001849062,"domain_scores_codex":[0.9970744,0.0001522998,0.0004933612,0.0009834206,0.0008264531,0.0004700943],"domain_scores_gemma":[0.9981154,0.0003017136,0.0004069547,0.0009208879,0.00007613353,0.0001788642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004012882,0.0002017427,0.001507337,0.00003506466,0.0001184987,0.00001144072,0.003498133,0.00002625221,0.9233923,0.0005649875,0.05343198,0.01681099],"study_design_scores_gemma":[0.001669247,0.0007691341,0.01704538,0.001201773,0.0003941098,0.0001382769,0.002781902,0.008369696,0.2095891,0.0009514093,0.7551602,0.00192983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4417011,0.000559304,0.0005650111,0.005048815,0.0006712981,0.0006916859,0.00008605648,0.0002040785,0.5504726],"genre_scores_gemma":[0.991787,0.0001047575,0.004697772,0.001061902,0.0004918926,1.636742e-7,0.00003863484,0.00009857066,0.001719315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7138032,"threshold_uncertainty_score":0.9999343,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2197439631","doi":"","title":"Biomass Estimation of Boreal Forests Using Single-Pass Polarimetric SAR Tomography at L-band","year":2014,"lang":"en","type":"article","venue":"EUSAR 2014; 10th European Conference on Synthetic Aperture Radar; Proceedings of","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Taiga; Remote sensing; Biomass (ecology); Polarimetry; Lidar; Environmental science; Boreal; L band; Tomography; Synthetic aperture radar; Forestry; Geology; Geography","authors":[{"name":"Yué Huang","is_ca":false},{"name":"Qiaoping Zhang","is_ca":false},{"name":"Marcus Schwaebisch","is_ca":false},{"name":"Ming Wei","is_ca":false},{"name":"Bryan Mercer","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01541196477915513,"gpt":0.2203213103420598,"spread":0.2049093455629047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006001916,0.0005139284,0.0006460095,0.000626718,0.0001358885,0.00006912689,0.0006191359,0.0002112823,0.000086655],"category_scores_gemma":[0.0003106286,0.0004505294,0.000210201,0.000663398,0.0003054749,0.0001795558,0.00009620542,0.0002668457,0.00004148843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095341,"about_ca_system_score_gemma":0.00002749921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000462066,"about_ca_topic_score_gemma":0.000002279532,"domain_scores_codex":[0.9976454,0.00004823443,0.000758155,0.0005478063,0.0005502027,0.0004502101],"domain_scores_gemma":[0.9983131,0.0002986848,0.0004277764,0.0004533567,0.0003246189,0.0001824391],"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":[0.0001776216,0.0006511499,0.003180848,0.001324687,0.0003718122,0.000004995961,0.0009372002,0.00003131678,0.4584453,0.04421699,0.006982818,0.4836752],"study_design_scores_gemma":[0.001289237,0.001118334,0.01483704,0.001739091,0.0004124762,0.0001536537,0.0001824629,0.06950338,0.6889721,0.002494039,0.2174129,0.001885275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4560316,0.0008075064,0.2816381,0.0004708829,0.0003361982,0.001296306,0.000173799,0.001070342,0.2581753],"genre_scores_gemma":[0.8650327,0.00005178575,0.1345858,0.00004322935,0.00006695214,0.000004730382,0.00002101567,0.0001275588,0.0000662095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4817899,"threshold_uncertainty_score":0.9997947,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}