{"id":"W2389336752","doi":"10.3390/rs8050397","title":"Improving Multiyear Sea Ice Concentration Estimates with Sea Ice Drift","year":2016,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"China Scholarship Council; Universität Bremen; Brigham Young University","keywords":"Environmental science; Snow; Brightness temperature; Sea ice; Climatology; Radiometric dating; Backscatter (email); Arctic; Remote sensing; Atmospheric sciences; Brightness; Meteorology; Geology; Oceanography; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001770488,0.0001550945,0.0001478582,0.00002690002,0.0002494808,0.00006559763,0.00007637553,0.0000654817,0.00008381386],"category_scores_gemma":[0.0001042376,0.00009710727,0.00003307849,0.0001129564,0.0001242616,0.0003091457,0.000009994387,0.0001084418,0.0001334734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001501252,"about_ca_system_score_gemma":0.00008132876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004926616,"about_ca_topic_score_gemma":0.001315467,"domain_scores_codex":[0.9989026,0.00004464967,0.0001623435,0.0002780962,0.0002331526,0.0003791594],"domain_scores_gemma":[0.9992327,0.0002904289,0.0001076352,0.0001730273,0.00007360175,0.0001225848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008148221,0.000004413681,0.0721549,0.00003869484,0.00002745835,0.0000764966,0.0003689555,0.001036889,0.004546312,0.00001489651,0.00003128731,0.9216182],"study_design_scores_gemma":[0.0004330744,0.00007729205,0.01807877,0.0001684308,0.00003604977,0.0001370052,0.0002250968,0.9790187,0.001105068,0.0001149777,0.0003725881,0.0002329477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8734659,0.00004189192,0.1228325,0.0006221587,0.0002102592,0.000144425,0.00002036916,0.000107827,0.00255468],"genre_scores_gemma":[0.9421634,0.0000281796,0.0572427,0.0002247394,0.0001479081,2.177262e-9,0.00003425508,0.000008218853,0.0001505873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9779818,"threshold_uncertainty_score":0.7447603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008114804435675584,"score_gpt":0.2002135660073967,"score_spread":0.1920987615717211,"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."}}