{"id":"W4391509534","doi":"10.1029/2023sw003652","title":"Improving Thermospheric Density Predictions in Low‐Earth Orbit With Machine Learning","year":2024,"lang":"en","type":"article","venue":"Space Weather","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trillium Therapeutics (Canada)","funders":"Natural Environment Research Council; Sight Research UK; National Aeronautics and Space Administration","keywords":"Low earth orbit; Orbit (dynamics); Earth (classical element); Astrobiology; Computer science; Physics; Aerospace engineering; Astronomy; Engineering; Satellite","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00099308,0.0005979916,0.0004299837,0.0004539491,0.000262545,0.0006454847,0.0009048622,0.0005579739,0.0008651983],"category_scores_gemma":[0.004267312,0.000294039,0.0003798516,0.0003697221,0.0003586627,0.0009393942,0.0006283398,0.0007627528,0.0004649087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006656059,"about_ca_system_score_gemma":0.000926991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02003995,"about_ca_topic_score_gemma":0.009996534,"domain_scores_codex":[0.9997776,0.00007903597,0.00001157198,0.00005233842,0.00004748274,0.00003203426],"domain_scores_gemma":[0.9984492,0.000925334,0.0001347384,0.0001263902,0.0002869174,0.00007738599],"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":[0.00003135815,0.0000372264,0.005205352,0.00001553607,0.0000221528,0.00001487491,0.00001606762,0.9789985,0.0004711793,0.0003284086,0.0007093903,0.01415013],"study_design_scores_gemma":[0.000001318431,0.000002828495,0.0002840928,0.000001499603,9.126416e-7,9.465458e-7,0.000001780237,0.9992858,0.0002116466,0.000154392,0.0000535939,0.000001287329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6758553,0.0005833338,0.309426,0.001078136,0.0001650846,0.00004441354,0.0009204485,0.006590472,0.005336829],"genre_scores_gemma":[0.9798775,0.00006833591,0.01839364,0.00009130735,0.0000401894,0.00002140974,0.0007030048,0.00009874854,0.0007058636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02003995,"threshold_uncertainty_score":0.0398466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0025213318582058,"score_gpt":0.1822500308412953,"score_spread":0.1797286989830895,"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."}}