{"id":"W7098214858","doi":"","title":"FOR PATH FOLLOWING OF OUTDOOR WHEELED MOBILE ROBOTS”","year":2011,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Permission; Acknowledgement; Path (computing); Field (mathematics); Control (management); Signature (topology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002155121,0.0006075948,0.0004085193,0.0003334803,0.0005557241,0.0005844045,0.0008551739,0.000759558,0.02471152],"category_scores_gemma":[0.0007317607,0.0002329401,0.0005468024,0.0003050952,0.0003207848,0.0009093818,0.00117669,0.0006728929,0.003786375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005208284,"about_ca_system_score_gemma":0.000555328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003655348,"about_ca_topic_score_gemma":0.005452436,"domain_scores_codex":[0.9998457,0.000025584,0.000005965082,0.00005084028,0.00004884145,0.00002297917],"domain_scores_gemma":[0.9998622,0.00003161316,0.00001360625,0.00003067426,0.00004323504,0.00001864773],"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":[0.0004193074,0.0001194474,0.002196482,0.0005859733,0.0001377483,0.0005391777,0.0003592054,0.3214537,0.02519512,0.09407759,0.07853763,0.4763787],"study_design_scores_gemma":[0.00003570165,0.0001671122,0.001321279,0.00004961413,0.00001513688,0.0002693477,0.0001071102,0.9102959,0.00660225,0.02808968,0.05301169,0.00003511113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04509548,0.001895075,0.8848622,0.001378445,0.001562122,0.0002837532,0.0010176,0.003218688,0.06068664],"genre_scores_gemma":[0.6192601,0.001000405,0.2882416,0.0004026396,0.0001463144,0.0003429404,0.001723673,0.00033858,0.08854372],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02471152,"threshold_uncertainty_score":0.08266824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02728442217005331,"score_gpt":0.2735435877628005,"score_spread":0.2462591655927472,"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."}}