{"id":"W4214505649","doi":"10.3390/rs14051123","title":"Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tundra; Interferometric synthetic aperture radar; Remote sensing; Synthetic aperture radar; Arctic; Environmental science; Decorrelation; Interferometry; Geology; Computer science; Oceanography; Algorithm","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":[0.0007140253,0.0005277545,0.0003195108,0.001686058,0.0001905852,0.0005657179,0.0002497094,0.0002908641,0.0004171523],"category_scores_gemma":[0.001053157,0.0001097747,0.0005493396,0.001285736,0.000149792,0.00030922,0.0002327126,0.0002606925,0.0001832407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003781446,"about_ca_system_score_gemma":0.0003691502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006693587,"about_ca_topic_score_gemma":0.005756564,"domain_scores_codex":[0.999725,0.00007487665,0.0000268504,0.00008961902,0.00005217426,0.00003143273],"domain_scores_gemma":[0.9996762,0.0001232724,0.00007440618,0.00003534315,0.00007157961,0.00001918878],"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":[0.0002745549,0.0008351588,0.1562927,0.0001194233,0.0003622379,0.0003925189,0.0002098136,0.2898103,0.02926617,0.001115507,0.001316131,0.5200053],"study_design_scores_gemma":[0.000005696222,0.00005756624,0.03913744,0.000006821888,0.00003343934,0.00004140031,0.00006577884,0.9574929,0.002374757,0.0003247077,0.0004452655,0.0000142139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8848059,0.0003668653,0.1113382,0.0001506804,0.00005704569,0.00009320205,0.000662956,0.000474964,0.002050132],"genre_scores_gemma":[0.9569739,0.0001622113,0.04167092,0.00003026983,0.00002966371,0.00005313352,0.0006557664,0.00001382335,0.0004103823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006693587,"threshold_uncertainty_score":0.0133093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02820354392823421,"score_gpt":0.2499786176451439,"score_spread":0.2217750737169097,"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."}}