{"id":"W4406542722","doi":"10.1007/s00190-024-01932-4","title":"A machine learning-based partial ambiguity resolution method for precise positioning in challenging environments","year":2025,"lang":"en","type":"article","venue":"Journal of Geodesy","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ambiguity resolution; Ambiguity; Computer science; Artificial intelligence; Resolution (logic); Computer vision; Geodesy; Geology; Global Positioning System; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005108129,0.00008702084,0.0001728648,0.0003049905,0.00007133246,0.00001976612,0.00009815671,0.00008977434,0.000008699531],"category_scores_gemma":[0.0001757612,0.00008719514,0.00008052102,0.0001512419,0.00001458735,0.00009315291,0.00001471622,0.0002586693,8.241666e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001816004,"about_ca_system_score_gemma":0.0000177264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008926479,"about_ca_topic_score_gemma":0.000009076878,"domain_scores_codex":[0.9992756,0.00004437492,0.0003361454,0.00007275079,0.0001068595,0.0001642475],"domain_scores_gemma":[0.999707,0.00009276804,0.00008381685,0.00007066118,0.00002398036,0.00002179362],"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":[0.00006913969,0.00002633526,0.001549211,0.00004442855,0.0000245351,0.000003191435,0.00006247604,0.9880471,0.002137497,0.0003056311,0.00006344086,0.007667067],"study_design_scores_gemma":[0.001066021,0.00007249156,0.001766529,0.0001409515,0.00002509055,0.000003939191,0.00005438231,0.9647723,0.02673873,0.0006466357,0.004632136,0.00008082085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0189162,0.0007279879,0.9795904,0.0002374415,0.0001870873,0.0001078302,0.000002930228,0.00005678304,0.0001733302],"genre_scores_gemma":[0.987129,0.0001145259,0.01261892,0.00002498799,0.00003144518,0.00001035217,0.000004976805,0.00001150464,0.00005431406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9682128,"threshold_uncertainty_score":0.3555714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009087918938346687,"score_gpt":0.2596814249852808,"score_spread":0.2505935060469341,"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."}}