{"id":"W4388585647","doi":"10.48550/arxiv.2311.05618","title":"Follow-up strategy of ILMT discovered supernovae","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Liège; Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS; Department of Science and Technology, Ministry of Science and Technology, India; York University","keywords":"Supernova; Sky; Telescope; Zenith; Physics; Astrophysics; Field of view; Light curve; Optics; Astronomy; Remote sensing; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008567343,0.000321963,0.0004133475,0.0002355819,0.00004077915,0.00003554146,0.0005195949,0.0002386264,0.00008275836],"category_scores_gemma":[0.00001933092,0.0003940344,0.000276671,0.0004102742,0.00009590029,0.000126407,0.0003283142,0.0004864471,0.0001381031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001271058,"about_ca_system_score_gemma":0.00006870702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000381913,"about_ca_topic_score_gemma":0.0001702431,"domain_scores_codex":[0.9988306,0.00003052973,0.0002244872,0.0004969581,0.00008258309,0.0003348823],"domain_scores_gemma":[0.9990025,0.00005809074,0.00007052928,0.0006992451,0.00006872805,0.000100914],"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":[0.00002988603,0.00002752862,0.003615903,0.000401476,0.0002992655,0.0002086105,0.000230977,0.9874744,0.001010054,0.004593254,0.001819833,0.0002887612],"study_design_scores_gemma":[0.003793826,0.0001124467,0.0275269,0.001096325,0.0008730692,0.00001171034,0.002906344,0.9093629,0.008062976,0.0407165,0.002454232,0.003082815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896508,0.00007355653,0.003944404,0.00001405695,0.001318667,0.0001703843,0.0002147703,0.0005453797,0.004068004],"genre_scores_gemma":[0.9918084,0.0002871597,0.00003141626,0.000005168323,0.00007786217,4.942349e-7,0.00008788677,0.00007754183,0.007624038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07811159,"threshold_uncertainty_score":0.9998512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08202960522740274,"score_gpt":0.180945954067656,"score_spread":0.09891634884025322,"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."}}