{"id":"W2170473703","doi":"10.5194/nhess-14-2503-2014","title":"Estimating velocity from noisy GPS data for investigating the temporal variability of slope movements","year":2014,"lang":"en","type":"article","venue":"Natural hazards and earth system sciences","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Eidgenössische Technische Hochschule Zürich","keywords":"Global Positioning System; Noise (video); Movement (music); Process (computing); Computer science; SIGNAL (programming language); Geodesy; Geology; Remote sensing; Data mining; Artificial intelligence; Acoustics; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003458323,0.0001358158,0.0002291563,0.00001515933,0.0006951697,0.0001113206,0.0007075409,0.00007356108,0.0000338755],"category_scores_gemma":[0.0003863182,0.00007073527,0.00003969593,0.0002331553,0.0006476213,0.0003626283,0.0004912813,0.0001412946,0.000005274793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001997175,"about_ca_system_score_gemma":0.00003552468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002324433,"about_ca_topic_score_gemma":0.0001776631,"domain_scores_codex":[0.9982569,0.0001294469,0.0003622825,0.0004971816,0.000490912,0.0002632336],"domain_scores_gemma":[0.998875,0.000369003,0.0002459719,0.0004066683,0.00002201484,0.00008132071],"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":[0.0000546678,0.00007438802,0.4446811,0.0004227196,0.0001048491,0.00000142721,0.002045951,0.008051607,0.005524908,0.003851857,0.001527254,0.5336593],"study_design_scores_gemma":[0.0002487474,0.00006741573,0.04165225,0.0001115621,0.00001845711,0.000002872555,0.0002999124,0.9552486,0.0001936181,0.001156904,0.0008701466,0.0001294885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930959,0.000101241,0.00353393,0.0002270486,0.000611732,0.0003333988,0.0001007424,0.00002886155,0.001967105],"genre_scores_gemma":[0.9621588,0.00000176881,0.03757379,0.00008612894,0.0001085287,0.000005962821,0.0000240331,0.00000461601,0.00003635312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.947197,"threshold_uncertainty_score":0.5346754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02059412436373644,"score_gpt":0.2565411047074087,"score_spread":0.2359469803436722,"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."}}