{"id":"W3157939862","doi":"10.1109/crv52889.2021.00012","title":"Accurate outdoor ground truth based on total stations","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Ground truth; Computer science; Total station; Artificial intelligence; Computer vision; Position (finance); Ranging; Landmark; Satellite system; Remote sensing; Kinematics; Real-time computing; Tracking (education); Prism; Global Positioning System; Geodesy; Geography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007108479,0.001204759,0.0007369459,0.001879475,0.0006457225,0.002110372,0.001444204,0.0008355181,0.005583019],"category_scores_gemma":[0.003885763,0.0004476812,0.000450689,0.003596589,0.0007556896,0.001951273,0.00203265,0.0006363431,0.005624419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005058658,"about_ca_system_score_gemma":0.001074032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005645591,"about_ca_topic_score_gemma":0.009006412,"domain_scores_codex":[0.9983565,0.0001995849,0.00006561865,0.0006398685,0.0005811809,0.0001572697],"domain_scores_gemma":[0.9974764,0.0001716359,0.000235294,0.001046637,0.0009991027,0.00007095325],"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":[0.0008544269,0.0002168954,0.02218609,0.0009499966,0.0002149276,0.0003599414,0.0008721509,0.1953715,0.119788,0.008615073,0.01030778,0.6402631],"study_design_scores_gemma":[0.0002084193,0.001038425,0.05188647,0.0004544015,0.0002287999,0.00102917,0.001561032,0.6451593,0.2245283,0.01701258,0.05658377,0.0003092054],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1960755,0.0004924575,0.7734839,0.00009567293,0.000236149,0.0001006954,0.004609114,0.01273831,0.01216814],"genre_scores_gemma":[0.7600798,0.0003375753,0.2258025,0.00007456145,0.0000359531,0.00009648949,0.008632489,0.000749218,0.004191379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005645591,"threshold_uncertainty_score":0.01867706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103287155833818,"score_gpt":0.2377415765622284,"score_spread":0.2167087050038902,"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."}}