{"id":"W3154283037","doi":"","title":"Material optimization for ladder climbing robot","year":2020,"lang":"en","type":"dissertation","venue":"eArsiv - Adu (Adnan Menderes University)","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Theoretical Astrophysics","keywords":"Robot; Climbing; Computer science; Mechanical engineering; Engineering; Artificial intelligence; Structural engineering","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":[0.0002266534,0.0006578361,0.0005571924,0.0005814618,0.0006295351,0.0005955895,0.0005352933,0.0008483778,0.007556962],"category_scores_gemma":[0.0004387025,0.000365061,0.0006523815,0.0003839899,0.000327763,0.0003575137,0.0006286135,0.0003895264,0.0009226341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005478559,"about_ca_system_score_gemma":0.0009996035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007631795,"about_ca_topic_score_gemma":0.007517857,"domain_scores_codex":[0.9998941,0.00001852055,0.000003885726,0.00001658154,0.00004091529,0.00002589342],"domain_scores_gemma":[0.9998785,0.00003940987,0.00002208624,0.000009778974,0.00003372294,0.00001643779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000646644,0.00003305602,0.0006055573,0.0001227203,0.00001771206,0.0001236498,0.0000273285,0.9690055,0.003366186,0.002090902,0.001346174,0.02319657],"study_design_scores_gemma":[0.00001567442,0.00008073314,0.0005294915,0.00001846204,0.000008931713,0.0000382239,0.00004011725,0.9949164,0.00054791,0.001858568,0.00193764,0.00000784033],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1481293,0.001679038,0.7718022,0.0007329074,0.0001746407,0.000194648,0.0004724377,0.001277981,0.07553678],"genre_scores_gemma":[0.8666748,0.0006188289,0.1063831,0.0001156031,0.0000333034,0.0002759676,0.0003524016,0.0001984375,0.02534757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007631795,"threshold_uncertainty_score":0.02528059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118615450887496,"score_gpt":0.1923614714626236,"score_spread":0.180499926373874,"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."}}