{"id":"W2333858146","doi":"10.1007/pl00012900","title":"Internet-Based Real-Time Kinematic Positioning","year":2002,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Core Research for Evolutional Science and Technology; Natural Sciences and Engineering Research Council of Canada; Beihang University","keywords":"Kinematics; Real Time Kinematic; The Internet; Computer science; Latency (audio); Base station; Real-time computing; Base (topology); Global Positioning System; Simulation; Telecommunications; GNSS applications; Operating system; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006551376,0.0005442883,0.00060697,0.001913036,0.0003556594,0.00128133,0.001203859,0.0009528347,0.01326932],"category_scores_gemma":[0.002183585,0.000269702,0.0001676612,0.002084166,0.0003201679,0.00133221,0.0009417219,0.0004572758,0.006927375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003269871,"about_ca_system_score_gemma":0.0005119699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002734449,"about_ca_topic_score_gemma":0.002979343,"domain_scores_codex":[0.9991931,0.000187123,0.00003899975,0.0001391578,0.000376861,0.00006472875],"domain_scores_gemma":[0.9987128,0.0003203738,0.0001179911,0.0003595504,0.0004208323,0.00006846615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001866821,0.0004395517,0.01104089,0.0002922696,0.0001027247,0.0002583228,0.0003143008,0.03321287,0.02422522,0.009478005,0.0253827,0.8933864],"study_design_scores_gemma":[0.00106328,0.001231182,0.0295935,0.0001706917,0.0003679066,0.001182546,0.0007065691,0.7640705,0.07557496,0.02062734,0.1051858,0.0002256864],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1343059,0.001268791,0.7549894,0.0007911397,0.0009155698,0.0002530949,0.002920795,0.03613681,0.06841835],"genre_scores_gemma":[0.8878077,0.0006158769,0.0861486,0.0001668581,0.0002449139,0.0001337158,0.002416032,0.0003354758,0.02213081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01326932,"threshold_uncertainty_score":0.04439026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652007680118612,"score_gpt":0.204693733326559,"score_spread":0.1881736565253729,"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."}}