{"id":"W1977158198","doi":"10.1046/j.1369-7412.2003.05220.x","title":"Estimation of Global Temperature Fields from Scattered Observations by a Spherical-Wavelet-Based Spatially Adaptive Method","year":2003,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Estimator; Wavelet; Spherical harmonics; Extrapolation; Smoothing; Thresholding; Representation (politics); Computer science; Algorithm; Surface (topology); Mathematics; Mathematical optimization; Artificial intelligence; Mathematical analysis; Computer vision; Geometry; Statistics; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003024742,0.0003410471,0.0009205615,0.00001798652,0.0002878813,0.0001442747,0.0009793096,0.0003542953,0.0001796027],"category_scores_gemma":[0.01102415,0.0002412213,0.0003785125,0.0005844038,0.0005560189,0.0002611906,0.0001578986,0.0008310744,0.00000301253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001949681,"about_ca_system_score_gemma":0.0005454643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002275029,"about_ca_topic_score_gemma":0.00001239751,"domain_scores_codex":[0.9919329,0.005205394,0.001183211,0.0004308956,0.0007896605,0.0004579048],"domain_scores_gemma":[0.9871966,0.01073624,0.0007833601,0.0004893905,0.0005235423,0.0002708762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001476227,0.00097988,0.0008078996,0.0001976124,0.001152399,0.0001446767,0.00118216,0.02804778,0.01316947,0.6994181,0.08307526,0.1703485],"study_design_scores_gemma":[0.002791414,0.001610405,0.020225,0.0001459956,0.0005514876,0.00009312462,0.0001879578,0.349588,0.01800282,0.6049831,0.001136779,0.0006838691],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001538998,0.0001604032,0.9941643,0.002458029,0.0007554879,0.0001973923,0.0006042888,0.00002624837,0.0000948785],"genre_scores_gemma":[0.03708835,0.00000500643,0.960997,0.001734869,0.0000717831,0.000006689987,0.00001419464,0.00001706897,0.00006500819],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3215403,"threshold_uncertainty_score":0.9973064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04839441782167431,"score_gpt":0.3182666349279302,"score_spread":0.2698722171062559,"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."}}