{"id":"W1578248138","doi":"10.1063/1.2844972","title":"Multirobot Lunar Excavation and ISRU Using Artificial-Neural-Tissue Controllers","year":2008,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Process (computing); Computer science; Task (project management); Ant colony; Automation; Artificial intelligence; Resource (disambiguation); Excavator; Artificial neural network; Survivability; Chassis; Engineering; Systems engineering; Ant colony optimization algorithms","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001014307,0.0002150121,0.0002476569,0.00009665969,0.0001841137,0.00009357923,0.0001289326,0.0001062925,0.00009449851],"category_scores_gemma":[0.00005230896,0.0002145153,0.00002892613,0.0001413873,0.0001129458,0.0003736553,0.00003043139,0.0001802656,0.00002300211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004138754,"about_ca_system_score_gemma":0.00001972365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006208345,"about_ca_topic_score_gemma":0.000003902682,"domain_scores_codex":[0.9989434,0.000004559104,0.000287462,0.0002741698,0.000178131,0.0003122729],"domain_scores_gemma":[0.9995463,0.00002126979,0.00005492258,0.00008468929,0.0001807806,0.0001119698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000460739,0.00005718901,0.01273363,0.0002357004,0.00007607451,0.0000308457,0.007275736,0.009203368,0.9248316,0.01091109,0.0006630499,0.03393566],"study_design_scores_gemma":[0.0002077685,0.00005493756,0.001519705,0.00005020859,0.00001931916,0.00006982485,0.0003924084,0.9618226,0.03417821,0.0008383409,0.0004926905,0.000353953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8796777,0.0002485372,0.1177701,0.0001390578,0.0001887586,0.0002886983,0.000002388531,0.000252846,0.001431976],"genre_scores_gemma":[0.9966376,0.0002525531,0.002767657,0.00006748657,0.00009738195,0.00001726008,0.000002529111,0.00003134992,0.0001262109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9526193,"threshold_uncertainty_score":0.874768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0557262960086207,"score_gpt":0.2546019858387874,"score_spread":0.1988756898301667,"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."}}