{"id":"W4210327302","doi":"10.1017/s0269888921000151","title":"Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots","year":2022,"lang":"en","type":"article","venue":"The Knowledge Engineering Review","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Norges Forskningsråd","keywords":"Neuroevolution; Computer science; Pruning; Modular design; Robustness (evolution); Artificial intelligence; Adaptability; Artificial neural network; Robot; Generalization; Machine learning; Mathematics","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":[0.001048387,0.0007253413,0.0007952705,0.0006306343,0.0002493454,0.0006684972,0.001204904,0.0008677205,0.0006451055],"category_scores_gemma":[0.002145157,0.0003740997,0.0006124268,0.0004714779,0.0009472048,0.0007044838,0.001092088,0.0007768636,0.0001571339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004753746,"about_ca_system_score_gemma":0.000333904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001123087,"about_ca_topic_score_gemma":0.0008171689,"domain_scores_codex":[0.9996272,0.0001175241,0.0000219771,0.00006321456,0.0001292473,0.0000407931],"domain_scores_gemma":[0.9992987,0.0003650374,0.0001167033,0.00008084644,0.0001053933,0.00003326584],"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":[0.00004373993,0.0000567428,0.0008819104,0.0002446351,0.0001447717,0.0001068589,0.0001033312,0.8478943,0.01932605,0.02105029,0.0005369475,0.1096105],"study_design_scores_gemma":[0.00001843413,0.0001288548,0.0004634815,0.00004703726,0.00003671131,0.00006284539,0.00001766394,0.9841121,0.003605437,0.009231819,0.002262871,0.00001279394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1610621,0.007061955,0.8246828,0.0004480317,0.00008920671,0.00007868066,0.00002187039,0.0003392859,0.006216056],"genre_scores_gemma":[0.8746082,0.002658566,0.1204482,0.00016999,0.00007400423,0.0001373165,0.00004563279,0.00007316731,0.00178482],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001204904,"threshold_uncertainty_score":0.005544484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838925561508074,"score_gpt":0.2297927903447874,"score_spread":0.2114035347297067,"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."}}