{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007980227,0.0003549703,0.0002832159,0.0003316528,0.0003299985,0.0007261958,0.0007871793,0.0004332185,0.004804688],"category_scores_gemma":[0.0003351098,0.0001873592,0.0001999463,0.0004956505,0.0004243911,0.0007809027,0.0005381579,0.0003899606,0.0008799256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002628867,"about_ca_system_score_gemma":0.00009826465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004243877,"about_ca_topic_score_gemma":0.0004471492,"domain_scores_codex":[0.9999141,0.00001622503,0.000003924293,0.0000309125,0.00002092825,0.00001384023],"domain_scores_gemma":[0.9998418,0.00004449902,0.00002656,0.00005179645,0.00001821656,0.00001701778],"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":[0.0007468453,0.0001535061,0.0006776167,0.0002067019,0.00006292697,0.0004232127,0.0002748397,0.4464137,0.1452037,0.06426974,0.009029579,0.3325377],"study_design_scores_gemma":[0.00008872959,0.0002893951,0.0003833756,0.00001539835,0.00002521236,0.0002342113,0.00006577155,0.9294438,0.03055425,0.02624347,0.01262811,0.00002809755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1010382,0.0003286469,0.8631305,0.0001745817,0.000178614,0.00006301849,0.00009285646,0.002769507,0.03222414],"genre_scores_gemma":[0.8927085,0.0002020902,0.09144881,0.00008072574,0.00004140823,0.00009122978,0.0001464194,0.0002070238,0.0150739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004804688,"threshold_uncertainty_score":0.01607335,"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."}}