{"id":"W4413887382","doi":"10.1109/tgrs.2025.3604644","title":"River Ice Fine-Grained Segmentation: A GF-2 Satellite Image Dataset and Deep Learning Benchmark","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Science Foundation of Ningbo; National Key Basic Research Program For Youth; National Natural Science Foundation of China","keywords":"Benchmark (surveying); GF(2); Satellite; Remote sensing; Deep learning; Computer science; Image segmentation; Artificial intelligence; Segmentation; Satellite image; Geology; Mathematics; Geodesy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009537493,0.002627568,0.0008455939,0.002731575,0.0008468457,0.001008099,0.002904943,0.001749336,0.002938586],"category_scores_gemma":[0.001630058,0.0003696379,0.001342151,0.002772257,0.0007282499,0.001101144,0.001224683,0.001270172,0.002426036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646535,"about_ca_system_score_gemma":0.001633846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06117876,"about_ca_topic_score_gemma":0.1089403,"domain_scores_codex":[0.999297,0.00006485109,0.00005454607,0.0002452211,0.0002157689,0.0001226461],"domain_scores_gemma":[0.999441,0.00009152887,0.00005272698,0.0001460146,0.0001957743,0.00007294235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001355501,0.001648815,0.02633386,0.002158833,0.0007503161,0.001209652,0.0002434872,0.1213672,0.01834795,0.002183097,0.6550071,0.1693943],"study_design_scores_gemma":[0.001118003,0.0007448589,0.08411352,0.0005629272,0.0004476576,0.001682489,0.0008621156,0.6448779,0.0419363,0.005149236,0.2181946,0.0003103601],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3970647,0.004876155,0.02114131,0.001631798,0.00104238,0.001281364,0.5316907,0.02181115,0.01946054],"genre_scores_gemma":[0.09867064,0.0005614047,0.02475622,0.0003087814,0.00008313339,0.0003491845,0.8716104,0.0004658969,0.003194375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06117876,"threshold_uncertainty_score":0.1216453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007012240920840388,"score_gpt":0.2245506412209264,"score_spread":0.2175384003000861,"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."}}