{"id":"W4311146610","doi":"10.1016/j.coldregions.2022.103736","title":"Deep learning based river surface ice quantification using a distant and oblique-viewed public camera","year":2022,"lang":"en","type":"article","venue":"Cold Regions Science and Technology","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Department of Atmospheric Sciences, Texas A and M University; China Scholarship Council; University of Alberta; Compute Canada","keywords":"Geology; Snow; Remote sensing; Environmental science; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002021664,0.0005429473,0.0004664733,0.000913163,0.0002448887,0.0006236697,0.0004985729,0.0004973706,0.002344742],"category_scores_gemma":[0.0002827373,0.00027586,0.0005363009,0.000778518,0.0002131408,0.000718588,0.0008121325,0.0006046011,0.0008644875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000331502,"about_ca_system_score_gemma":0.0008251825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01119564,"about_ca_topic_score_gemma":0.02431392,"domain_scores_codex":[0.9998164,0.000009995543,0.000004832815,0.00005754337,0.00004919885,0.00006199333],"domain_scores_gemma":[0.9998789,0.00001129177,0.00001272421,0.00001916315,0.00005757634,0.00002026589],"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":[0.0005323263,0.0005028023,0.02410338,0.0002128156,0.0002446677,0.0004408419,0.0001991253,0.1321485,0.1823182,0.002257699,0.01164876,0.6453909],"study_design_scores_gemma":[0.00001952629,0.00005572084,0.01593584,0.00002151428,0.0000565166,0.0001216605,0.0000835368,0.9559885,0.02429547,0.0007315487,0.002660472,0.00002969008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5400473,0.0006986974,0.4369931,0.0004076718,0.0002584273,0.000120294,0.003196852,0.003747528,0.01453022],"genre_scores_gemma":[0.8498964,0.0003532151,0.1388638,0.0001470256,0.00008806611,0.00004484425,0.003373295,0.0001513753,0.00708208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01119564,"threshold_uncertainty_score":0.0222609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02093815362091582,"score_gpt":0.2255295450109897,"score_spread":0.2045913913900739,"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."}}