{"id":"W2799893902","doi":"10.1117/12.2303014","title":"DRESH: DRone EnSnaring mesH","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Drone; Obstacle; Computer science; Aeronautics; Notional amount; Systems engineering; Aerospace engineering; Control engineering; Engineering; Simulation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001239202,0.0004828166,0.0003374319,0.0006067305,0.0004975304,0.0007928605,0.0008399392,0.0004884455,0.08339152],"category_scores_gemma":[0.0004132716,0.0001736418,0.000286357,0.0003474355,0.0002616355,0.000974632,0.001720103,0.0005446837,0.0214277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003282906,"about_ca_system_score_gemma":0.000334518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002095758,"about_ca_topic_score_gemma":0.004160195,"domain_scores_codex":[0.9998676,0.000009775436,0.000005313545,0.00002343534,0.00007368251,0.00002023017],"domain_scores_gemma":[0.9998499,0.00002510839,0.00000914127,0.00005466037,0.00003469786,0.00002644098],"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":[0.000542882,0.000111804,0.002534278,0.000676552,0.0000420048,0.0007940038,0.0003636423,0.03869679,0.03854124,0.04958272,0.2215493,0.6465647],"study_design_scores_gemma":[0.00006515261,0.0001521274,0.001289238,0.000101814,0.00001355482,0.0003810239,0.0001919264,0.06788342,0.01770682,0.007964578,0.904213,0.00003721553],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04569175,0.00189798,0.3057017,0.001122327,0.002120504,0.0004518372,0.009527694,0.05634772,0.5771385],"genre_scores_gemma":[0.3855898,0.001660231,0.1278411,0.0006375376,0.0002738952,0.00032346,0.0180846,0.005181698,0.4604077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08339152,"threshold_uncertainty_score":0.2789724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01965874536530586,"score_gpt":0.2553516366605523,"score_spread":0.2356928912952465,"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."}}