{"id":"W2898109356","doi":"10.13679/j.advps.2018.3.00181","title":"Determination of Arctic melt pond fraction and sea ice roughness from Unmanned Aerial Vehicle (UAV) imagery","year":2018,"lang":"en","type":"article","venue":"ADVANCES IN POLAR SCIENCE","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sea ice; Arctic ice pack; Geology; Arctic; Surface roughness; Sea ice thickness; Melt pond; Digital elevation model; Drift ice; Elevation (ballistics); Remote sensing; Oceanography; Geometry; Materials science","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":[0.0001191013,0.0002222197,0.000168307,0.001645397,0.0001610969,0.0002457021,0.0001051367,0.0001232954,0.0002668262],"category_scores_gemma":[0.0004478383,0.0001074497,0.0002605144,0.0006563028,0.0001048931,0.0002594638,0.0001599243,0.00009393462,0.00007630442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001597173,"about_ca_system_score_gemma":0.0001455271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009985747,"about_ca_topic_score_gemma":0.01833171,"domain_scores_codex":[0.9999263,0.000007758445,0.000004945725,0.00001935582,0.00002433568,0.00001740092],"domain_scores_gemma":[0.9998462,0.00003458504,0.00003223283,0.00001654377,0.00005385838,0.00001654275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003602809,0.0001145909,0.5983583,0.0001758243,0.000188887,0.0006963583,0.0005373691,0.06553344,0.145488,0.0004342406,0.001194498,0.1869181],"study_design_scores_gemma":[0.000008467372,0.00004364026,0.8367181,0.00001425992,0.00004616452,0.0001507272,0.0001887005,0.1533724,0.008681418,0.0001027884,0.0006521026,0.0000211405],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930489,0.00007660188,0.005657425,0.000007119168,0.000004077646,0.00001535873,0.0005038714,0.0001188995,0.0005678883],"genre_scores_gemma":[0.9921375,0.00005773761,0.00704295,0.000003294149,0.000003196534,0.000008338622,0.0006346503,0.000009170447,0.0001031906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009985747,"threshold_uncertainty_score":0.01985526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005805670260906052,"score_gpt":0.2415536659148549,"score_spread":0.2357479956539489,"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."}}