{"id":"W2130678039","doi":"10.14430/arctic609","title":"Climatic Variability in the Kuparuk Region, North-central Alaska: Optimizing Spatial and Temporal Intepolation in a Sparse Observation Network","year":2003,"lang":"en","type":"article","venue":"ARCTIC","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interpolation (computer graphics); Multivariate interpolation; Digital elevation model; Elevation (ballistics); Environmental science; Spatial variability; Remote sensing; Spatial distribution; Kriging; Meteorology; Geology; Geography; Statistics; Computer science; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009045057,0.0001016258,0.0001303644,0.0000404476,0.00009054979,0.00005633059,0.0000788683,0.00004874333,0.0002038052],"category_scores_gemma":[0.0002072148,0.00007725802,0.00002200146,0.0003322289,0.00004794382,0.0002402784,0.000005891186,0.0001647822,0.000008376792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001406422,"about_ca_system_score_gemma":0.00002619261,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01675795,"about_ca_topic_score_gemma":0.3220976,"domain_scores_codex":[0.9986907,0.0004177435,0.0002699533,0.0001950942,0.0001217188,0.0003048524],"domain_scores_gemma":[0.9992912,0.0004241008,0.00007544224,0.0001484953,0.00001604943,0.00004467161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002289028,0.00001833133,0.9940647,0.00002638181,0.000001887457,0.00001090256,0.001984718,0.002823074,7.39681e-7,0.00003281399,0.00003144747,0.0009821334],"study_design_scores_gemma":[0.0002540213,0.0000357254,0.9481311,0.00003978443,0.000007570965,0.00001918538,0.0002760674,0.04984552,4.25733e-7,0.00112604,0.0001767232,0.0000878045],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998393,0.0001172036,0.00022066,0.0004526463,0.0002230391,0.0002791456,0.00001483155,0.000007976688,0.0002915107],"genre_scores_gemma":[0.9985605,0.0001035642,0.0004995118,0.0003619743,0.0000803733,0.000003530714,0.000383153,0.000002525861,0.000004894806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3053396,"threshold_uncertainty_score":0.9897895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05315566374076099,"score_gpt":0.2308266903298688,"score_spread":0.1776710265891078,"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."}}