{"id":"W2010258063","doi":"10.1109/lcnw.2014.6927734","title":"Utilizing Sprouts WSN platform for equipment detection and localization in harsh environments","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Shovel; Crusher; Engineering; Computer science; Automotive engineering; Real-time computing; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001543362,0.0004799002,0.000261274,0.0004483855,0.0001810984,0.0002859762,0.0005160091,0.000227995,0.001156761],"category_scores_gemma":[0.0001978149,0.0001518466,0.0001518973,0.0002395873,0.0001450386,0.0005247789,0.0005415611,0.0002016107,0.0004620939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002264251,"about_ca_system_score_gemma":0.000402788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003351497,"about_ca_topic_score_gemma":0.006924842,"domain_scores_codex":[0.9998289,0.00001923431,0.000006103975,0.00004072196,0.00008498516,0.00001999774],"domain_scores_gemma":[0.9998838,0.0000168987,0.0000213309,0.00001733075,0.00004330307,0.00001733469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004772952,0.0001774573,0.01164833,0.0004197372,0.0000666385,0.001477856,0.0004168618,0.06776477,0.5552945,0.003380686,0.008747094,0.3501287],"study_design_scores_gemma":[0.0001159677,0.001272587,0.0241803,0.0001014261,0.0001209585,0.001506996,0.0007112927,0.5139176,0.361655,0.002458844,0.09384889,0.0001101312],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2792685,0.0004220073,0.69326,0.0003621274,0.0002007543,0.0002339277,0.0004993366,0.01129644,0.01445695],"genre_scores_gemma":[0.8533469,0.0004803595,0.1290529,0.000153479,0.0000450829,0.0001729848,0.0007827954,0.000107442,0.01585808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003351497,"threshold_uncertainty_score":0.006663978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203478584556476,"score_gpt":0.2057532081372499,"score_spread":0.1937184222916852,"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."}}