{"id":"W645572830","doi":"","title":"Developments in teracomputing : proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology : Reading, UK, November 13-17, 2000","year":2001,"lang":"en","type":"book","venue":"WORLD SCIENTIFIC eBooks","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Supercomputer; Fortran; Scalability; Computer science; Subroutine; Meteorology; Computational science; Parallel computing; Geography; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00181411,0.0003340101,0.0005093032,0.0005576374,0.0003149938,0.0001840117,0.0008416825,0.0001316465,0.0001965437],"category_scores_gemma":[0.00003849395,0.0002198325,0.0001018966,0.0006491017,0.0004417955,0.00010205,0.000129891,0.000758244,0.00002418834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006108383,"about_ca_system_score_gemma":0.0003323322,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006456045,"about_ca_topic_score_gemma":0.01878297,"domain_scores_codex":[0.9970399,0.00006789764,0.0008045926,0.0006391628,0.0008594355,0.0005889804],"domain_scores_gemma":[0.9983223,0.0003602751,0.0007188194,0.0003627025,0.0001741683,0.0000617118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001608592,0.0001310594,0.8674828,0.0002204015,0.0001360432,0.00001285637,0.007503294,0.01447703,0.0001614257,0.001717045,0.03068331,0.07731389],"study_design_scores_gemma":[0.000599972,0.00006321266,0.9234722,0.003764431,0.00004290128,0.000005719849,0.0001154636,0.004951015,0.0005045768,0.001153516,0.06471636,0.0006106324],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7596163,0.00001971631,7.091211e-7,0.00003714018,0.000918621,0.0005084535,0.00002291448,0.000007711334,0.2388685],"genre_scores_gemma":[0.6093261,5.183251e-7,0.0001539519,0.0001044771,0.00003799862,0.000001610115,0.00002109284,0.000007380774,0.3903469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1514784,"threshold_uncertainty_score":0.9991217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04279199223131529,"score_gpt":0.2130496266723956,"score_spread":0.1702576344410803,"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."}}