{"id":"W4281809583","doi":"10.1080/10106049.2022.2082556","title":"Spatially varying WIndow based maximum likelihood feature tracking (SWIFT) method for glacier surface velocity estimations","year":2022,"lang":"en","type":"article","venue":"Geocarto International","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glacier; Mean squared error; Feature (linguistics); Remote sensing; Synthetic aperture radar; Geology; Satellite; Tracking (education); Computer science; Mathematics; Geomorphology; Engineering; Statistics","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":[0.000574861,0.0006501826,0.0004855266,0.001032682,0.0003173024,0.0004141446,0.0005583407,0.0004945784,0.001094307],"category_scores_gemma":[0.001758554,0.0003191525,0.0005777061,0.001082286,0.0001700912,0.0006309265,0.0003322186,0.0006344347,0.0004498246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002652493,"about_ca_system_score_gemma":0.0006847202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005248435,"about_ca_topic_score_gemma":0.005795313,"domain_scores_codex":[0.9997223,0.00004454657,0.00002105457,0.00007741619,0.0001068435,0.00002777855],"domain_scores_gemma":[0.9995623,0.0001586426,0.00008685158,0.0000393926,0.0001385594,0.00001414701],"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":[0.0002954192,0.0001341289,0.0123374,0.0002628793,0.0001571891,0.0003494347,0.0002948287,0.2479037,0.07578319,0.002748804,0.005384159,0.6543489],"study_design_scores_gemma":[0.00001685342,0.00004153232,0.006257674,0.00001449711,0.00003055347,0.0001496715,0.00003594638,0.9716898,0.01817521,0.000828781,0.002723842,0.00003553003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04179906,0.0003320273,0.9552963,0.00004736885,0.00004283306,0.0000379247,0.0002215794,0.001580025,0.0006427757],"genre_scores_gemma":[0.4028517,0.0004157792,0.5930555,0.00004707912,0.00004197059,0.0001259843,0.001054945,0.0002848292,0.002122235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005248435,"threshold_uncertainty_score":0.01043576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0239551223821528,"score_gpt":0.2646878311545926,"score_spread":0.2407327087724398,"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."}}