{"id":"W4385559495","doi":"10.1017/eds.2023.22","title":"Environmental sensor placement with convolutional Gaussian neural processes","year":2023,"lang":"en","type":"article","venue":"Environmental Data Science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute","funders":"Natural Environment Research Council; Engineering and Physical Sciences Research Council; Sight Research UK; UK Research and Innovation; Alan Turing Institute; National Science Foundation","keywords":"Computer science; Gaussian process; Convolutional neural network; Scalability; Gaussian; Flexibility (engineering); Kriging; Artificial intelligence; Machine learning; Probabilistic logic; Anomaly detection; Sampling (signal processing); Data mining; Process (computing); Computer vision; Statistics; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004373988,0.0002760907,0.0001639231,0.0001886883,0.0007075232,0.0003930258,0.003970802,0.00003946712,0.0001846225],"category_scores_gemma":[0.00003019135,0.0002201664,0.0000198057,0.001142958,0.001292085,0.004068795,0.00262331,0.0001702648,0.0009321672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001570823,"about_ca_system_score_gemma":0.0002454552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007455243,"about_ca_topic_score_gemma":0.000004422596,"domain_scores_codex":[0.996179,0.00002837644,0.0002605146,0.001395557,0.00135811,0.0007784358],"domain_scores_gemma":[0.9979798,0.00005787578,0.0001306823,0.001524989,0.000006154416,0.0003005558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000453075,0.005090686,0.3522173,0.0008607078,0.0003298466,0.004003393,0.009746279,0.01141205,0.3494504,0.04601667,0.02571781,0.1947017],"study_design_scores_gemma":[0.002051304,0.0009708026,0.660558,0.0001231084,0.00004154575,0.0008377553,0.002142676,0.2946794,0.01321736,0.0008719897,0.02234652,0.002159562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8058684,0.0004649872,0.1792326,0.005012352,0.000908946,0.001369928,0.002589601,0.001126724,0.003426476],"genre_scores_gemma":[0.9874138,0.0001266568,0.01147996,0.0002753368,0.00005202451,0.00002595598,0.0002753904,0.00001460575,0.0003362928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3362331,"threshold_uncertainty_score":0.9998457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121041352193427,"score_gpt":0.2344499535427245,"score_spread":0.2132395400207902,"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."}}