{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004984466,0.0004082524,0.0003707474,0.0001765455,0.0002834955,0.0003414605,0.0005402572,0.0003015113,0.001305196],"category_scores_gemma":[0.002124522,0.0001631838,0.0001333023,0.0002726486,0.0002824059,0.0005053879,0.0004325713,0.0005868225,0.0002121279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000436263,"about_ca_system_score_gemma":0.0006087358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004500934,"about_ca_topic_score_gemma":0.007345148,"domain_scores_codex":[0.9997018,0.00009121018,0.000013728,0.00007242923,0.00005866908,0.00006218659],"domain_scores_gemma":[0.9990146,0.0006162987,0.0001265122,0.00007058649,0.00009033248,0.00008160844],"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":[0.0005397253,0.0002390366,0.002883022,0.00009656035,0.00003967238,0.0001030573,0.0001271072,0.8680465,0.01539592,0.003456245,0.002590351,0.1064827],"study_design_scores_gemma":[0.00001972448,0.00008519621,0.0009535857,0.00000280388,0.000006493435,0.00001747185,0.0000278043,0.9929833,0.00338792,0.001630945,0.0008792504,0.000005539371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2917086,0.0007109118,0.7001148,0.0005130858,0.0001445505,0.0001035859,0.0002065789,0.0007574301,0.005740471],"genre_scores_gemma":[0.9645374,0.0001362276,0.03330489,0.00006820749,0.00004763153,0.00004153407,0.0001190368,0.00005626482,0.001688756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004500934,"threshold_uncertainty_score":0.008949459,"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."}}