{"id":"W4410434111","doi":"10.1371/journal.pone.0323112","title":"A criterion for assessing obstacle-induced environmental complexity in multi-robot coverage exploration","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Austrian Science Fund","keywords":"Obstacle; Robot; Metric (unit); Context (archaeology); Computer science; Obstacle avoidance; Computational complexity theory; Mobile robot; Artificial intelligence; Simulation; Algorithm; Engineering; Geography","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":[0.001337684,0.0008387167,0.0006525584,0.002436465,0.000585785,0.001035618,0.0007634603,0.0007028399,0.0007834769],"category_scores_gemma":[0.01084818,0.00023464,0.0005284736,0.001310248,0.001126469,0.001756508,0.002236899,0.0005495892,0.0002300349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007123327,"about_ca_system_score_gemma":0.0007237473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001179575,"about_ca_topic_score_gemma":0.001400621,"domain_scores_codex":[0.9976961,0.0004183825,0.000154115,0.0002707945,0.001292304,0.0001681748],"domain_scores_gemma":[0.989926,0.005595247,0.001732566,0.0007648503,0.001550983,0.000430302],"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":[0.0004762431,0.0002840698,0.05965098,0.0009872617,0.0002812648,0.000676223,0.000820147,0.6233852,0.09041685,0.02301951,0.00191718,0.1980852],"study_design_scores_gemma":[0.00003301297,0.001148615,0.08712912,0.0001633474,0.00009869828,0.00145834,0.000536414,0.8301487,0.05723236,0.01643457,0.005346339,0.0002704058],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2545955,0.000708312,0.7373996,0.0001415893,0.00005369757,0.000183145,0.0003939496,0.0005083136,0.00601584],"genre_scores_gemma":[0.8782721,0.0001977654,0.1202356,0.00003344269,0.00002598242,0.0001581158,0.0004073184,0.00007288453,0.0005966559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002436465,"threshold_uncertainty_score":0.007074416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2222702906321481,"score_gpt":0.3079801227500602,"score_spread":0.08570983211791208,"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."}}