{"id":"W3085894599","doi":"","title":"MegaPipe: the MegaCam Image Stacking Pipeline","year":2009,"lang":"en","type":"article","venue":"American Astronomical Society Meeting Abstracts #217","topic":"Astronomical Observations and Instrumentation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Calibration; Computer science; Image processing; Pipeline transport; Computer vision; Remote sensing; Image (mathematics); Artificial intelligence; Environmental science; Geology; Mathematics; Statistics; Environmental engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003621333,0.0002983238,0.0003146056,0.00002338654,0.0002697699,0.0001766303,0.0003385173,0.00006430581,0.00004659407],"category_scores_gemma":[0.00005046938,0.0002549485,0.0002739139,0.0002176063,0.0002875433,0.0003623239,0.00004107178,0.0005085728,0.0000858418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002750213,"about_ca_system_score_gemma":0.00003396918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001895238,"about_ca_topic_score_gemma":0.000007210479,"domain_scores_codex":[0.9982304,0.00003637908,0.0006214019,0.0003145894,0.0001981213,0.000599095],"domain_scores_gemma":[0.9990665,0.0001732396,0.0002216105,0.0003200958,0.00004634428,0.0001722188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00003755134,0.0001764492,0.00852384,0.00001962389,0.0001979815,7.983353e-7,0.0009947625,0.6209487,0.04482646,0.0002601358,0.02098548,0.3030283],"study_design_scores_gemma":[0.001073809,0.0002336154,0.6258144,0.00007394034,0.0001201132,0.000002488498,0.004532562,0.3326389,0.0142646,0.0001143364,0.02015174,0.000979512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859394,0.00004352654,0.008020507,0.001772614,0.0001537872,0.0002019847,0.00001756926,0.000338514,0.00351205],"genre_scores_gemma":[0.9532617,0.00001638145,0.04578116,0.0003420521,0.0004484747,0.00001384095,0.00005498539,0.00004075324,0.00004068881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6172906,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006635885790111637,"score_gpt":0.219608363343534,"score_spread":0.2129724775534224,"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."}}