{"id":"W2087027772","doi":"10.1177/0278364913478897","title":"The Canadian planetary emulation terrain 3D mapping dataset","year":2013,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency; Institute for Christian Studies; University of Toronto","funders":"","keywords":"Terrain; Emulation; Scripting language; Computer science; Robotics; Artificial intelligence; Field (mathematics); Panning (audio); Robot; Computer vision; Computer graphics (images); Engineering; Cartography; Geography; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001308325,0.00007399727,0.0000762694,0.000271467,0.0003141811,0.0004544703,0.0008393746,0.00004847251,0.00009023642],"category_scores_gemma":[0.00019295,0.0000452653,0.00003207795,0.0001531275,0.00008984439,0.0001689752,0.00004696588,0.0005062787,0.0001016003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003061166,"about_ca_system_score_gemma":0.0001553405,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009455094,"about_ca_topic_score_gemma":0.02035952,"domain_scores_codex":[0.9982864,0.0001092979,0.000327931,0.00005788691,0.0009523854,0.0002661184],"domain_scores_gemma":[0.9985336,0.0004207501,0.00005813014,0.0001812432,0.0006801942,0.0001260717],"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":[0.00000562036,0.000005167137,0.000276436,0.000003256871,0.00008617555,0.00001758401,0.0001645048,0.9372266,0.0002984916,0.0008929091,0.05560705,0.005416207],"study_design_scores_gemma":[0.0001604458,0.00002347468,0.00330582,0.00003606682,0.000004692802,0.00006911492,0.0002057685,0.9510424,0.00007425481,0.001795463,0.04321402,0.00006846775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2177605,0.005173471,0.4330996,0.2923363,0.01992378,0.003607267,0.00107087,0.0002046125,0.02682358],"genre_scores_gemma":[0.9966227,0.0002424041,0.002086981,0.0001720728,0.0005289204,0.000002409084,0.0001921227,0.00001912125,0.0001332524],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7788622,"threshold_uncertainty_score":0.9975164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05919476706959579,"score_gpt":0.311463517561405,"score_spread":0.2522687504918092,"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."}}