{"id":"W2582616273","doi":"10.1007/978-3-319-53058-1_7","title":"A 2-Approximation Algorithm for Barrier Coverage by Weighted Non-uniform Sensors on a Line","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Generalization; Computer science; Line (geometry); Algorithm; Approximation algorithm; Simple (philosophy); Perimeter; Line segment; Mathematics; Artificial intelligence","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":[0.001035134,0.001930328,0.002408777,0.001036583,0.0007134162,0.001521699,0.00372563,0.00203587,0.008325899],"category_scores_gemma":[0.004564946,0.0008213823,0.001218999,0.001708659,0.0007588046,0.002532243,0.003154111,0.002412705,0.001683998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001587695,"about_ca_system_score_gemma":0.001626306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00424347,"about_ca_topic_score_gemma":0.003860196,"domain_scores_codex":[0.9989214,0.0002555902,0.00005808425,0.000232284,0.0003197054,0.0002128731],"domain_scores_gemma":[0.9979125,0.001277236,0.0001182345,0.0002974163,0.0002448758,0.0001498075],"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":[0.0009336183,0.0002892667,0.0007268337,0.0003636332,0.0001144524,0.0001350004,0.000238546,0.7466708,0.006077074,0.03040578,0.01294585,0.201099],"study_design_scores_gemma":[0.00005006837,0.00004905101,0.00005276793,0.000008609414,0.000009901609,0.0000339538,0.0000194732,0.9916831,0.0005951065,0.006650709,0.0008407512,0.00000648173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0176719,0.000352701,0.976151,0.0002890362,0.0001038731,0.0001475976,0.0001931524,0.001235622,0.003855083],"genre_scores_gemma":[0.2120501,0.0002593357,0.7781699,0.0001984463,0.0000860948,0.0005299778,0.0007186246,0.0003876677,0.007599971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008325899,"threshold_uncertainty_score":0.02785295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185709224394747,"score_gpt":0.2392769737842001,"score_spread":0.2274198815402526,"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."}}