{"id":"W4383176201","doi":"10.48550/arxiv.2307.00713","title":"Designing a Magnetic Micro-Robot for Transporting Filamentous Microcargo","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Payload (computing); Nanotechnology; Computer science; Process (computing); Targeted drug delivery; Drug delivery; Materials science; Mechanical engineering; Simulation; Engineering; Artificial intelligence","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":[0.0001720837,0.0003308383,0.0002135529,0.000227017,0.0002940892,0.0002327378,0.0004206989,0.0004063501,0.0007998995],"category_scores_gemma":[0.0001840422,0.0001421903,0.0001763603,0.0001016818,0.0003319773,0.0003256782,0.0002339874,0.0001666005,0.0003845769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002020464,"about_ca_system_score_gemma":0.0002993494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003251911,"about_ca_topic_score_gemma":0.0005254914,"domain_scores_codex":[0.999912,0.00001118297,0.000005838619,0.00002326467,0.00003554872,0.00001211484],"domain_scores_gemma":[0.9999222,0.000015078,0.00002364938,0.00000844021,0.00001551432,0.00001516965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007507715,0.00007683798,0.0005815508,0.0002698334,0.00001157247,0.0002960695,0.00009374885,0.01096859,0.9572288,0.00309268,0.0003445783,0.02696062],"study_design_scores_gemma":[0.0001047459,0.001726162,0.004323464,0.00005696929,0.00005008962,0.001072418,0.0001575368,0.2176176,0.7505081,0.00132419,0.02298487,0.00007383533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4306322,0.001454072,0.5587924,0.000373698,0.0002008036,0.0005426877,0.000117144,0.0009801211,0.006906791],"genre_scores_gemma":[0.6131017,0.0005760862,0.3819699,0.0001064591,0.0000225739,0.0003140995,0.00006613675,0.00003822353,0.003804862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007998995,"threshold_uncertainty_score":0.002675891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08562335691065188,"score_gpt":0.1976528446167311,"score_spread":0.1120294877060792,"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."}}