{"id":"W4401413872","doi":"10.1109/icra57147.2024.10610998","title":"RTS-GT: Robotic Total Stations Ground Truthing dataset","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ground truth; Computer science; Artificial intelligence; Remote sensing; Computer vision; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007945138,0.002185061,0.001018586,0.001796628,0.0007726243,0.0007571115,0.002952063,0.001565556,0.004685672],"category_scores_gemma":[0.002950249,0.0003298013,0.0009963518,0.002846075,0.0006150887,0.00104652,0.001525359,0.001270828,0.007348466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008604627,"about_ca_system_score_gemma":0.001470854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02024393,"about_ca_topic_score_gemma":0.03984337,"domain_scores_codex":[0.9987238,0.0001697131,0.000136055,0.0004047578,0.0004338655,0.0001317925],"domain_scores_gemma":[0.9984791,0.0002173341,0.0001689739,0.0005343843,0.0005109684,0.00008915889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009064864,0.0007669266,0.01872396,0.003074953,0.0004845554,0.0005214181,0.0002636432,0.07539174,0.009924674,0.00363138,0.7556101,0.1307002],"study_design_scores_gemma":[0.0008411289,0.001070984,0.07935617,0.0007262161,0.0002850225,0.001323721,0.001204492,0.2090496,0.02878521,0.01061069,0.6663089,0.0004377768],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06046186,0.001372957,0.03167506,0.0004955074,0.0004787823,0.0004701406,0.8739627,0.02132956,0.009753324],"genre_scores_gemma":[0.04098744,0.0001741223,0.01543871,0.00008815266,0.00002306452,0.0002519656,0.9416148,0.0002850407,0.001136651],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02024393,"threshold_uncertainty_score":0.04025221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346231765252091,"score_gpt":0.2338795940584995,"score_spread":0.2204172764059786,"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."}}