{"id":"W2991585608","doi":"","title":"Getting Science Beyond the Research Community: Examples of Education and Outreach from the IceCube Project","year":2011,"lang":"en","type":"article","venue":"DESY (CERN, DESY, Fermilab, IHEP, and SLAC)","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Marsden Fund; Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Fonds Wetenschappelijk Onderzoek; Deutsche Forschungsgemeinschaft; Belgian Federal Science Policy Office; Office of Polar Programs; Polarforskningssekretariatet; U.S. Department of Energy; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Vetenskapsrådet; National Science Foundation","keywords":"Outreach; Sociology; Political science; Pedagogy; Public relations","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002423258,0.0001378796,0.0001369261,0.000100238,0.001711602,0.0002355987,0.0007308567,0.00004541664,0.0001064319],"category_scores_gemma":[0.0001307708,0.00008282953,0.00002308127,0.0003056112,0.0013285,0.0003648147,0.000268183,0.0003627597,0.00001983819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001303704,"about_ca_system_score_gemma":0.00006289232,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08143169,"about_ca_topic_score_gemma":0.003467525,"domain_scores_codex":[0.9982725,0.0004281795,0.0002069083,0.0003026714,0.0004586948,0.0003310472],"domain_scores_gemma":[0.998634,0.0005452977,0.00008442816,0.0006067718,0.0000410889,0.00008843112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003146929,0.0000795091,0.8192211,0.00002854607,0.00001993455,0.000001597063,0.01033625,0.000001796649,0.0002265143,0.0002730628,0.0005647007,0.1692155],"study_design_scores_gemma":[0.0001296772,0.0001316718,0.9756957,0.00004258527,0.00002682337,0.000008231683,0.0163929,0.0001849722,0.0004738077,0.002201506,0.004594296,0.0001178395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808804,0.002147349,0.000009719711,0.0001881711,0.0002126032,0.0003074327,0.00007839865,0.00001821179,0.0161577],"genre_scores_gemma":[0.9979762,0.00079587,0.0006090543,0.0001789605,0.0001067758,0.000004572664,0.00008235559,0.000006436868,0.0002398008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1690976,"threshold_uncertainty_score":0.999588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.162305846079025,"score_gpt":0.3073173126349337,"score_spread":0.1450114665559087,"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."}}