{"id":"W2142736626","doi":"10.5194/gi-3-187-2014","title":"A framework for benchmarking of homogenisation algorithm performance on the global scale","year":2014,"lang":"en","type":"article","venue":"Geoscientific instrumentation, methods and data systems","topic":"Climate variability and models","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Environment and Climate Change Canada","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Met Office; Department for Environment, Food and Rural Affairs, UK Government; National Science Foundation","keywords":"Benchmarking; Strengths and weaknesses; Scale (ratio); Computer science; Best practice; Product (mathematics); Data science; Data mining; Operations research; Industrial engineering; Geography; Mathematics; Engineering; Business; Economics; Cartography; Marketing","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":[0.005484635,0.0001006043,0.0001418898,0.00002122951,0.0004293807,0.0001263727,0.0003565161,0.00005720569,0.00007865117],"category_scores_gemma":[0.0001838919,0.00007252796,0.00002507187,0.0002414537,0.0002330062,0.0002950423,0.0002150917,0.000050335,0.000006787232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005911077,"about_ca_system_score_gemma":0.00001061118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004237318,"about_ca_topic_score_gemma":0.00002450871,"domain_scores_codex":[0.9984481,0.0003075365,0.00032923,0.0004679215,0.0002641531,0.0001830601],"domain_scores_gemma":[0.9986048,0.0004249318,0.0001912176,0.0007105767,0.00002057101,0.00004790461],"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":[0.00003798922,0.0001287152,0.03696399,0.0002088729,0.00002455612,5.174805e-8,0.001336877,0.001687096,0.001456643,0.03601272,0.0009895485,0.9211529],"study_design_scores_gemma":[0.0002494912,0.0001095075,0.01154338,0.00007633123,0.00003422784,0.000005841611,0.0006720545,0.9636316,0.0006772987,0.01168936,0.01116695,0.000143984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3658904,0.000009127442,0.6319556,0.00007529913,0.000842847,0.000401176,0.0005080928,0.000009260299,0.0003082174],"genre_scores_gemma":[0.3832121,0.00001459407,0.6161534,0.00008605207,0.00007986412,0.00006196625,0.0003437254,0.000006145602,0.00004217589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9619445,"threshold_uncertainty_score":0.3302493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05991476897614453,"score_gpt":0.3549512300505387,"score_spread":0.2950364610743942,"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."}}