{"id":"W4306752936","doi":"","title":"Navigating with highly precise odometry and noisy GPS: a case study *","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Historical Geography and Cartography","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Odometry; Global Positioning System; Artificial intelligence; Computer vision; Computer science; Visual odometry; Geodesy; Geography; Robot; Mobile robot; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.006719276,0.0003293524,0.0004140805,0.0001826829,0.001440343,0.0004764988,0.0007868928,0.0002895009,0.00006781115],"category_scores_gemma":[0.0009754265,0.0002813391,0.0001901018,0.001042057,0.000842977,0.0001906428,0.0006877801,0.0008230633,0.000009080144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000110802,"about_ca_system_score_gemma":0.0002523833,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0173658,"about_ca_topic_score_gemma":0.02135609,"domain_scores_codex":[0.9905673,0.006933442,0.0004316732,0.0008932094,0.0007178126,0.0004565442],"domain_scores_gemma":[0.9944215,0.001852225,0.0004494422,0.001316118,0.001568108,0.000392563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006357047,0.002837469,0.1538481,0.0002423976,0.000480717,0.000726281,0.4736742,0.000005224846,0.0002009765,0.04183875,0.0006101874,0.3254721],"study_design_scores_gemma":[0.01986215,0.00007449231,0.05036376,0.04471129,0.003345129,0.001211853,0.2678728,0.001120772,0.007467298,0.05541583,0.5337645,0.01479003],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9426975,0.001803212,0.008784083,0.00323503,0.0002513504,0.001044591,0.00005175022,0.0003147886,0.04181769],"genre_scores_gemma":[0.9920446,0.0003828486,0.005687282,0.0000362809,0.00005764123,0.0001455774,0.00002109181,0.00003464733,0.001590003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5331544,"threshold_uncertainty_score":0.9999639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393914489694042,"score_gpt":0.2665527412171648,"score_spread":0.2526135963202244,"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."}}