{"id":"W2527113450","doi":"10.1007/b136266","title":"Computational Science and Its Applications – ICCSA 2005","year":2005,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":394,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Computational science; Theoretical computer science","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":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.01521055,0.0004284639,0.0005051605,0.002979648,0.00121299,0.003115332,0.006141687,0.0001541931,0.000102845],"category_scores_gemma":[0.002745886,0.0003549141,0.00007930264,0.005170301,0.003036813,0.001066445,0.003691214,0.000571127,0.0004744316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007322654,"about_ca_system_score_gemma":0.00341411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006250115,"about_ca_topic_score_gemma":0.00005584786,"domain_scores_codex":[0.9880105,0.00007479484,0.0009993091,0.003408439,0.006555441,0.0009515148],"domain_scores_gemma":[0.9928994,0.002730391,0.0004808559,0.001922319,0.001555634,0.0004114197],"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":[0.000002580264,0.0000426368,0.00003461459,0.0000106002,0.000003313103,0.000006602525,0.0002193616,0.1583534,0.00002153808,0.00487271,0.004497381,0.8319352],"study_design_scores_gemma":[0.000170315,0.00002954767,0.000484041,0.00007273084,0.000007053318,0.00003399382,0.000001184631,0.7484295,0.00004714791,0.09910429,0.1512152,0.0004049245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002685393,0.0007229735,0.9863691,0.001895695,0.001517705,0.0007355591,0.0000467607,0.0001029745,0.008340739],"genre_scores_gemma":[0.2959874,0.0001110255,0.6539756,0.009554438,0.004793376,0.0001438286,0.000112826,0.0001075913,0.03521393],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8315303,"threshold_uncertainty_score":0.9998903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05567509002823586,"score_gpt":0.3527477389872752,"score_spread":0.2970726489590394,"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."}}