{"id":"W4286218410","doi":"10.5194/gmd-2022-178","title":"A new bootstrap technique to quantify uncertainty in estimates of ground surface temperature and ground heat flux histories from geothermal data","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alexander von Humboldt-Stiftung","keywords":"Bootstrapping (finance); Geothermal gradient; Earth system science; Singular value decomposition; Heat flux; Climate model; Environmental science; Proxy (statistics); Climate change; Climatology; Global temperature; Sampling (signal processing); Econometrics; Meteorology; Computer science; Geology; Geophysics; Statistics; Global warming; Mathematics; Geography; Heat transfer; 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":[0.003266474,0.0007274619,0.0004992466,0.001837724,0.0003511687,0.0005734436,0.000789577,0.0007389452,0.001036239],"category_scores_gemma":[0.0131611,0.0002290101,0.0009002747,0.001212757,0.0005994384,0.001075412,0.000973586,0.0008380637,0.0004337876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002916221,"about_ca_system_score_gemma":0.000450087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001626892,"about_ca_topic_score_gemma":0.001410379,"domain_scores_codex":[0.9986933,0.0004835805,0.00008519389,0.0001431069,0.0005320634,0.00006272854],"domain_scores_gemma":[0.9946399,0.003021505,0.0004746003,0.0006563167,0.001103553,0.0001040688],"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":[0.0005432844,0.0002619106,0.01558071,0.0003509404,0.000516028,0.0005406298,0.0003839435,0.4158335,0.06729739,0.03006241,0.002756794,0.4658724],"study_design_scores_gemma":[0.00001138937,0.00008051498,0.002448491,0.00001865641,0.00002830026,0.0001347652,0.00002505624,0.9820766,0.00953593,0.004494045,0.001122216,0.00002390425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03225257,0.0001766475,0.9663695,0.00004440725,0.00005187165,0.00004691257,0.0001160608,0.0003749859,0.0005671543],"genre_scores_gemma":[0.4702607,0.0002760848,0.5272036,0.00007616568,0.0001237741,0.0002025967,0.001060741,0.000203813,0.0005924677],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003266474,"threshold_uncertainty_score":0.01727492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05680117442754749,"score_gpt":0.302553874061308,"score_spread":0.2457526996337605,"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."}}