{"id":"W2754104704","doi":"10.1145/3131893","title":"TrailSense","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Cluster analysis; Generalizability theory; Artificial intelligence; Crowdsensing; Climb; Data mining; Machine learning; Pattern recognition (psychology); Computer security; Statistics; Mathematics; Engineering","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":[0.0004065996,0.001378361,0.0008494168,0.00137441,0.0009461625,0.002239655,0.001640175,0.00118043,0.1242191],"category_scores_gemma":[0.001650888,0.0005234471,0.0006145413,0.0012312,0.0003417846,0.00245159,0.00267728,0.0009835932,0.09242789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004367118,"about_ca_system_score_gemma":0.0009789006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005350707,"about_ca_topic_score_gemma":0.0102804,"domain_scores_codex":[0.9993197,0.00003645985,0.00003932134,0.0001573829,0.0003535409,0.00009360971],"domain_scores_gemma":[0.9994028,0.00005942342,0.00003702642,0.0001566786,0.0002736626,0.0000703719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001236821,0.0001937332,0.005772257,0.001235796,0.00007624336,0.0007559619,0.0005568478,0.003593028,0.02513678,0.01058078,0.4913909,0.4594709],"study_design_scores_gemma":[0.0001339452,0.0001988909,0.003483971,0.0001736252,0.00005394603,0.0005251506,0.0003421763,0.01559477,0.009158583,0.004920004,0.9653262,0.00008881609],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03507841,0.003686956,0.185967,0.00162263,0.002384432,0.001410995,0.069507,0.192537,0.5078055],"genre_scores_gemma":[0.2810464,0.003390167,0.1051858,0.001710815,0.0003886197,0.001133732,0.139838,0.01245275,0.4548537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1242191,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0143219757470816,"score_gpt":0.2718296595039684,"score_spread":0.2575076837568868,"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."}}