{"id":"W4375852255","doi":"10.20944/preprints202305.0527.v1","title":"Scheduling LEO Satellite Transmissions for Remote Water Level Monitoring","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Satellite Communication Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université Laval; Montfort Hospital","funders":"","keywords":"Geostationary orbit; Satellite; Computer science; Low earth orbit; Scheduling (production processes); Remote sensing; Real-time computing; Ground station; Energy consumption; Communications satellite; Environmental science; Meteorology; Geography; Engineering; Aerospace engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001338442,0.000618948,0.0007046736,0.0003527389,0.0002109466,0.00008438972,0.001552497,0.0006906589,0.00007497454],"category_scores_gemma":[0.0001645726,0.0006110541,0.0004567979,0.0001845667,0.00005189799,0.0001312551,0.001101503,0.001319823,0.002633925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003112903,"about_ca_system_score_gemma":0.00007119026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001419887,"about_ca_topic_score_gemma":0.000009630508,"domain_scores_codex":[0.9966539,0.0001328077,0.001122778,0.0009367245,0.000380722,0.0007731024],"domain_scores_gemma":[0.9961214,0.000295709,0.0001265236,0.002976349,0.000226326,0.0002536681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008200441,0.00008528954,0.1017823,0.006200373,0.001365201,0.00002114585,0.01514934,0.2708095,0.5755793,0.0001643296,0.00003223664,0.02872894],"study_design_scores_gemma":[0.0007480705,0.000009851427,0.06886268,0.002897036,0.0001608238,0.00001158806,0.0005457272,0.03257279,0.8220452,0.006385928,0.06406222,0.001698081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.914699,0.004606022,0.06050317,0.0006137763,0.006852863,0.002770873,0.0001311261,0.004763321,0.005059902],"genre_scores_gemma":[0.9744635,0.005562244,0.01530741,0.00001126004,0.0006876955,0.0004036575,0.0001960097,0.0003764837,0.002991755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2464658,"threshold_uncertainty_score":0.9996341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4345589825237237,"score_gpt":0.3935115992866972,"score_spread":0.0410473832370265,"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."}}