{"id":"W3110112816","doi":"10.1007/978-3-030-63000-3_13","title":"Line Reconfiguration by Programmable Particles Maintaining Connectivity","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Control reconfiguration; Line (geometry); Node (physics); Grid; Plane (geometry); Basis (linear algebra); Algorithm; Topology (electrical circuits); Distributed computing; Mathematics; Geometry; Physics; Combinatorics; Embedded system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002929099,0.0002718148,0.0002825911,0.0001059047,0.00009159598,0.0002027052,0.0003966781,0.0001579124,0.00004571179],"category_scores_gemma":[0.00005716266,0.0002611546,0.00004943196,0.0001756659,0.0001811455,0.0001714679,0.0000847374,0.0004851068,0.00003490807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001288852,"about_ca_system_score_gemma":0.00005994699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001271284,"about_ca_topic_score_gemma":0.00007245472,"domain_scores_codex":[0.998553,0.00001104957,0.0002806774,0.0005303121,0.0002858207,0.000339115],"domain_scores_gemma":[0.9993517,0.000119265,0.00006089824,0.0002944848,0.00006226447,0.0001114014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002660206,0.000004777921,0.00001179556,0.00002735143,0.000007299308,0.00001489744,0.0001791564,0.3417664,0.001390795,0.001499077,0.00003626839,0.6550595],"study_design_scores_gemma":[0.0000636842,0.0001014502,0.000005183648,0.000126898,0.000005460887,0.00001060062,2.311214e-7,0.937691,0.03307744,0.02563467,0.002938942,0.0003444446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004356181,0.0004855301,0.9962392,0.0003161443,0.0005236289,0.0002529913,0.000006182041,0.0002199937,0.001520738],"genre_scores_gemma":[0.9568355,0.0000789261,0.04233552,0.0003080874,0.0002983236,0.00001074244,0.00001515692,0.00004421333,0.00007353457],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9563999,"threshold_uncertainty_score":0.9999841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02189548540352779,"score_gpt":0.230946272193073,"score_spread":0.2090507867895452,"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."}}