{"id":"W2498633402","doi":"","title":"Positioning accuracy and availability analysis of three commercial WADGPS services","year":2000,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geography; Forestry; Humanities; Cartography; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009763159,0.00006253228,0.0001447069,0.00006858102,0.0001178727,0.00004453462,0.0003003162,0.00002279915,0.0001111052],"category_scores_gemma":[0.000005886145,0.0000591957,0.00004762032,0.0005969426,0.00004137641,0.0002371619,0.00007977533,0.00003945519,0.00001094499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008840182,"about_ca_system_score_gemma":0.000009777246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000497607,"about_ca_topic_score_gemma":0.00003052426,"domain_scores_codex":[0.9993764,0.00001750856,0.0002092342,0.0001798327,0.0001248596,0.00009214532],"domain_scores_gemma":[0.9993331,0.0001701709,0.00007191307,0.0003308425,0.00005623219,0.00003772907],"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":[0.0000103009,0.0002330612,0.00965283,0.00009107188,0.0003195293,0.000001655253,0.001238139,0.009241994,0.0003865369,0.3172452,0.0001020632,0.6614776],"study_design_scores_gemma":[0.00008950672,0.00002892744,0.2725407,0.00001614951,0.0001015655,0.000002970059,0.000008248037,0.5758588,0.0002637408,0.1501315,0.0008397091,0.0001182306],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3609971,0.00002585301,0.6359201,0.0008792808,0.000004890767,0.000109862,0.00001204222,0.0000962014,0.001954681],"genre_scores_gemma":[0.8192426,0.000007059521,0.1805085,0.0001861527,0.000007436041,0.00001592161,0.00001811318,0.000001994813,0.00001222159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6613594,"threshold_uncertainty_score":0.2413931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151705705104038,"score_gpt":0.27334885849039,"score_spread":0.2618318014393496,"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."}}