{"id":"W4280566752","doi":"10.3390/rs14102378","title":"Efficient Shallow Network for River Ice Segmentation","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Block (permutation group theory); Artificial intelligence; Remote sensing; Geology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002994738,0.00007650982,0.00008708883,0.00002631335,0.0006842784,0.00001954827,0.00006205004,0.00001800826,0.0002118688],"category_scores_gemma":[0.00001609139,0.00007545103,0.00005391584,0.0001397846,0.00003291366,0.00002282038,0.00001647852,0.00010042,0.00002151363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001841057,"about_ca_system_score_gemma":0.00003103859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005508271,"about_ca_topic_score_gemma":0.0001069941,"domain_scores_codex":[0.9991924,0.00005574902,0.000120505,0.0001739747,0.0001954038,0.0002619942],"domain_scores_gemma":[0.9996365,0.0001404974,0.00006315614,0.00009000984,0.00002186823,0.00004791521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004164708,0.00000292038,0.001447228,0.000008894493,0.00001096493,0.000009489366,0.000555824,0.6969048,0.00003166114,0.0000205092,0.0001801064,0.300786],"study_design_scores_gemma":[0.00019564,0.00005861388,0.004378827,0.000007066303,0.00001924127,0.00004310859,0.0006188385,0.9900466,0.000003537493,0.0008178963,0.003705899,0.0001047204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8830319,0.00006171026,0.1135052,0.000340146,0.0008523772,0.0002647119,0.00003544968,0.00004729677,0.00186119],"genre_scores_gemma":[0.9093145,0.00000504381,0.08936623,0.0006771751,0.0002146869,8.852602e-9,0.0001632012,0.000004544067,0.0002545631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3006813,"threshold_uncertainty_score":0.5262986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01274160953342524,"score_gpt":0.2158999543821573,"score_spread":0.2031583448487321,"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."}}