{"id":"W1977350399","doi":"10.1109/crv.2014.29","title":"Trajectory Inference Using a Motion Sensing Network","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Victoria","funders":"","keywords":"Software deployment; Trajectory; Inference; Computer science; Wireless sensor network; Probabilistic logic; Markov chain; Real-time computing; Range (aeronautics); Hidden Markov model; Dynamic Bayesian network; Fidelity; Inference engine; Bayesian network; Artificial intelligence; Computer network; Machine learning; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003712627,0.0001385756,0.0001468632,0.00006590246,0.0001758556,0.0001474708,0.0003756412,0.00007913045,0.00000852957],"category_scores_gemma":[0.00004434856,0.0001306985,0.00005150555,0.0004795996,0.00003711885,0.0002679383,0.0001531038,0.0001364024,0.00002279079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004583962,"about_ca_system_score_gemma":0.00002361962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005718905,"about_ca_topic_score_gemma":0.00003453055,"domain_scores_codex":[0.9986329,0.0001596808,0.0001973602,0.0003694814,0.0002323991,0.0004081775],"domain_scores_gemma":[0.9990296,0.0002083047,0.00008050117,0.000521358,0.0000699056,0.00009030566],"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":[7.593198e-7,0.00000947387,0.000277686,0.000002277127,0.000003332114,0.000002059204,0.00005633698,0.9131458,0.0006291963,0.04256191,0.00008171167,0.0432294],"study_design_scores_gemma":[0.0001006991,0.00001801481,0.0006466071,0.00002907888,0.000002990133,0.00001217939,0.000003372989,0.9971561,0.0005828856,0.000738789,0.0005403719,0.0001689318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0816784,0.00002290275,0.9111101,0.00006635387,0.0006642318,0.00004701965,3.551141e-8,0.000366728,0.006044257],"genre_scores_gemma":[0.7165309,0.000001719694,0.2828279,0.0003001926,0.0002580312,2.755324e-7,5.467193e-7,0.000008362435,0.00007219282],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6348524,"threshold_uncertainty_score":0.5329732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02024282606394912,"score_gpt":0.2377155362985602,"score_spread":0.217472710234611,"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."}}