{"id":"W1505138002","doi":"10.48550/arxiv.0904.2568","title":"MegaPipe: the MegaCam image stacking pipeline","year":2009,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Astronomical Observations and Instrumentation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Computer science; Calibration; Computer vision; Image processing; Pipeline transport; Image (mathematics); Remote sensing; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001647444,0.0002567971,0.0002223694,0.00005314667,0.0001006121,0.00009876276,0.0003431597,0.0001514382,0.0001437215],"category_scores_gemma":[0.00002471877,0.000209436,0.000121302,0.0001001637,0.00004138987,0.0001984845,0.0001548055,0.0006376772,0.0001651141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001458077,"about_ca_system_score_gemma":0.000028591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004391528,"about_ca_topic_score_gemma":0.00001398723,"domain_scores_codex":[0.9989313,0.00002960068,0.0003960517,0.0002493273,0.0001350299,0.0002586146],"domain_scores_gemma":[0.9992731,0.00003717021,0.00009112082,0.0004867503,0.00005526849,0.00005655864],"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":[0.00005249907,0.0002559506,0.243471,0.0006507665,0.0006480673,0.00001973507,0.002050241,0.4226446,0.03658231,0.002816186,0.05563362,0.2351751],"study_design_scores_gemma":[0.0007572579,0.00004915984,0.7417958,0.0002995176,0.0001945865,0.000002810604,0.0003368738,0.2046516,0.01682372,0.00171803,0.03228219,0.001088476],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959837,0.0002909855,0.03414766,0.0007855911,0.0006441344,0.0002965441,0.00002768198,0.0002974986,0.003672968],"genre_scores_gemma":[0.9947348,0.0001709826,0.003895713,0.0002044748,0.0005202658,0.00003985975,0.0001949961,0.00004633214,0.000192567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4983248,"threshold_uncertainty_score":0.8540552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03022463567200089,"score_gpt":0.2462575745231742,"score_spread":0.2160329388511733,"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."}}