{"id":"W2804450181","doi":"10.48550/arxiv.1805.06922","title":"Preparing the NIRSpec/JWST science data calibration: from ground testing to sky","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Calibration and Measurement Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Airbus Defense and Space; Canadian Space Agency; Goddard Space Flight Center; European Space Agency; National Aeronautics and Space Administration","keywords":"James Webb Space Telescope; Spectrograph; Calibration; Sky; Remote sensing; Computer science; Physics; Astronomy; Telescope; Geology; Spectral line","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":[],"consensus_categories":[],"category_scores_codex":[0.0004846912,0.0002313462,0.0001826863,0.0001705893,0.0002991991,0.0003544723,0.002113903,0.0001269339,0.00005934091],"category_scores_gemma":[0.0001813928,0.0002387085,0.00004244924,0.0007663668,0.0002097234,0.0006933144,0.002207516,0.000344535,0.00004530736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002710291,"about_ca_system_score_gemma":0.0001831567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004064015,"about_ca_topic_score_gemma":0.0003603469,"domain_scores_codex":[0.9984767,0.00003792972,0.0001939138,0.000834843,0.0001748168,0.0002817773],"domain_scores_gemma":[0.9976168,0.0000842403,0.00007722426,0.001921029,0.0001458443,0.0001548899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007950317,0.0001541985,0.0112339,0.0002826671,0.0003749402,0.00009649171,0.002183137,0.8590219,0.02872407,0.03388203,0.05819715,0.005770034],"study_design_scores_gemma":[0.0001269387,0.00002016594,0.002246906,0.0001827793,0.0000752939,0.000001644116,0.000117849,0.9823139,0.004217137,0.004236979,0.006008913,0.0004514836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.411536,0.00007061871,0.5549149,0.0001141389,0.001276292,0.0008704492,0.0001629598,0.002088497,0.0289661],"genre_scores_gemma":[0.9962093,0.0000246246,0.00302837,0.0001050391,0.000359821,0.000002058073,0.00007602175,0.00003095295,0.0001638394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5846732,"threshold_uncertainty_score":0.9734251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1852883546912763,"score_gpt":0.2190509313380143,"score_spread":0.03376257664673796,"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."}}