{"id":"W1994729982","doi":"10.1002/sat.704","title":"Neural network implementation of a fade countermeasure controller for a VSAT link","year":2002,"lang":"en","type":"article","venue":"International Journal of Satellite Communications","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ste. Anne's Hospital","funders":"","keywords":"Computer science; Throughput; Link adaptation; Controller (irrigation); Real-time computing; Fading; Artificial neural network; Communications satellite; Computer network; Channel (broadcasting); Telecommunications; Engineering; Satellite; Artificial intelligence; Wireless","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":[0.0003477064,0.0004258718,0.0002184105,0.0002695837,0.0003741441,0.000570646,0.0008976938,0.0005685493,0.002977696],"category_scores_gemma":[0.0009573181,0.0001443766,0.0001506309,0.0001357233,0.0002889848,0.000333568,0.0002291947,0.0004754828,0.0004287419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005962746,"about_ca_system_score_gemma":0.0005346819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005829525,"about_ca_topic_score_gemma":0.00529563,"domain_scores_codex":[0.9998572,0.00002013338,0.000009426138,0.0000390438,0.00005064024,0.00002355769],"domain_scores_gemma":[0.9997215,0.00007931756,0.00003190727,0.00002384802,0.0001286166,0.000014788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005709942,0.0002686855,0.002519068,0.0001771677,0.0001066347,0.0002288194,0.0001426991,0.2870979,0.06928494,0.006823106,0.002484339,0.6302956],"study_design_scores_gemma":[0.00003875086,0.0001700362,0.0005533785,0.00001250365,0.00003227616,0.00004496096,0.00001018818,0.9763416,0.02053791,0.0006725256,0.001574225,0.00001174694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1304475,0.000288066,0.856707,0.0003138914,0.0002405459,0.0002172844,0.00006120518,0.003753948,0.00797052],"genre_scores_gemma":[0.8921027,0.00007784678,0.1029365,0.0001153702,0.00003261929,0.0001032479,0.00004203385,0.00002976718,0.004559994],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005829525,"threshold_uncertainty_score":0.01159114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04415560594562633,"score_gpt":0.3125806708951662,"score_spread":0.2684250649495399,"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."}}