{"id":"W2272175853","doi":"10.1002/rob.21616","title":"Three‐dimensional Scan Registration using Curvelet Features in Planetary Environments","year":2015,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Neptec Design Group (Canada); University of Waterloo","funders":"","keywords":"Curvelet; Feature (linguistics); Pattern recognition (psychology); Metric (unit); Histogram; Transformation (genetics); Feature extraction; Image registration; Matching (statistics)","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":[0.00016363,0.00008284316,0.0001365648,0.00009512009,0.00001675573,0.00001940824,0.00006892079,0.00009752937,0.000008285427],"category_scores_gemma":[0.00004090379,0.0000767628,0.00003216587,0.00006623258,0.00001035379,0.0001063446,0.000007483728,0.0002266701,0.000001587884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007910758,"about_ca_system_score_gemma":0.00003558285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003560486,"about_ca_topic_score_gemma":0.0001026368,"domain_scores_codex":[0.9992819,0.0000160287,0.0003020124,0.00005052875,0.0002391702,0.0001104005],"domain_scores_gemma":[0.9996855,0.00003858041,0.00009055599,0.00007940078,0.00002626096,0.00007967419],"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":[0.00001777737,0.00001901901,0.004208382,0.000008041803,0.00001400257,0.00005143957,0.00003821449,0.9913545,0.0006421066,0.00008588452,0.003186695,0.0003739869],"study_design_scores_gemma":[0.0006263301,0.000150566,0.007594812,0.00008597615,0.00002628074,0.0001576327,0.00003497252,0.9891483,0.001051428,0.0006842482,0.0002927422,0.0001467541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3371445,0.001239894,0.658344,0.0008729691,0.0016854,0.0001289603,0.00000331146,0.00002001806,0.0005609589],"genre_scores_gemma":[0.9732437,0.00004451897,0.02632824,0.0001174307,0.0002236948,1.003124e-7,0.000009072966,0.00001364349,0.00001967755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6360992,"threshold_uncertainty_score":0.3130296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02666331061201527,"score_gpt":0.2306653545073566,"score_spread":0.2040020438953414,"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."}}