{"id":"W2544159747","doi":"10.1109/acssc.2007.4487512","title":"Optimal Beamforming with Mobile Robots","year":2007,"lang":"en","type":"article","venue":"Conference record/Conference record - Asilomar Conference on Signals, Systems, & Computers","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mobile robot; Computer science; Beamforming; Robot; Human–computer interaction; Artificial intelligence; Telecommunications","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","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001482826,0.002037639,0.002416059,0.00121551,0.0005464566,0.001193323,0.001879669,0.0008418914,0.0004850317],"category_scores_gemma":[0.00008156649,0.001972677,0.000347352,0.001309028,0.0004263541,0.00167833,0.0002617872,0.001491999,0.0005589151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009332554,"about_ca_system_score_gemma":0.0007663291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004391096,"about_ca_topic_score_gemma":0.0004483953,"domain_scores_codex":[0.9907482,0.00038578,0.002744993,0.002143174,0.001306434,0.002671432],"domain_scores_gemma":[0.9933771,0.0007386408,0.001138844,0.001857478,0.001672125,0.001215755],"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":[0.000697356,0.0003815915,0.00204598,0.001300287,0.0007726506,0.0004222976,0.002110168,0.5029759,0.005943334,0.009577384,0.001905557,0.4718675],"study_design_scores_gemma":[0.002814273,0.002641691,0.0002907671,0.005801555,0.000191244,0.0004134779,0.00546904,0.9562857,0.002296455,0.0002544675,0.01926822,0.004273121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05917098,0.0003206943,0.9140428,0.00005979428,0.0040204,0.003026928,0.00008791815,0.002046535,0.01722399],"genre_scores_gemma":[0.9209198,0.0006378899,0.07564758,0.00007885735,0.0006917869,0.0005671478,0.0001941478,0.0003465293,0.0009162531],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8617488,"threshold_uncertainty_score":0.9998435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02756860017434402,"score_gpt":0.2513101351621163,"score_spread":0.2237415349877723,"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."}}