{"id":"W2150708363","doi":"10.1109/rose.2011.6058524","title":"Robust pseudo-random fiducial marker for indoor localization","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Fiducial marker; Computer vision; Computer science; Artificial intelligence; Robustness (evolution); Coding (social sciences); Redundancy (engineering); Decoding methods; Mathematics; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.00009303016,0.0001068362,0.000114402,0.00006100179,0.00005380346,0.00002164953,0.00006265482,0.00008880273,0.0003965652],"category_scores_gemma":[0.00002963108,0.00009921882,0.00005009665,0.0001096603,0.00001427039,0.00008621786,0.000006581934,0.00003827765,0.00002956152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002406141,"about_ca_system_score_gemma":0.000008886819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002146778,"about_ca_topic_score_gemma":0.00003058485,"domain_scores_codex":[0.9994324,0.00001083556,0.0001871916,0.0001199694,0.00007866516,0.0001709305],"domain_scores_gemma":[0.9997159,0.00002727631,0.00001702916,0.0001230795,0.00006640349,0.00005033975],"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":[0.0001236454,0.0000490718,0.002189107,0.00009727834,0.00004970472,0.000001867581,0.0003542383,0.9705078,0.0004251807,0.008561601,0.01431836,0.003322117],"study_design_scores_gemma":[0.0009132099,0.0000284119,0.0006673181,0.000009030473,0.00001889215,0.000001183106,0.00002886419,0.9915699,0.003544939,0.0004226165,0.00263856,0.0001570144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005669198,0.00002049417,0.9790161,0.0000141041,0.0004170481,0.0002760451,0.000005351516,0.0002269175,0.01435472],"genre_scores_gemma":[0.9732818,0.00002604382,0.02558021,0.0001534251,0.0001829459,0.00003428937,0.00007170478,0.00005579429,0.0006137959],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9676126,"threshold_uncertainty_score":0.434211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03541470106754002,"score_gpt":0.1996216683915614,"score_spread":0.1642069673240214,"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."}}