{"id":"W4414918015","doi":"10.3390/s25196203","title":"Sensor Input Type and Location Influence Outdoor Running Terrain Classification via Deep Learning Approaches","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Inertial measurement unit; Deep learning; Convolutional neural network; SIGNAL (programming language); Acceleration; Preprocessor; Terrain; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007011016,0.000832085,0.000364987,0.0004489648,0.0001770121,0.0005892838,0.0004715624,0.000484365,0.001324077],"category_scores_gemma":[0.002114719,0.0002477485,0.0004925317,0.0003721478,0.0002026758,0.0005300306,0.0005631453,0.0005815548,0.0004220102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002768523,"about_ca_system_score_gemma":0.0003112953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004537447,"about_ca_topic_score_gemma":0.006825891,"domain_scores_codex":[0.9996982,0.00007344057,0.00001662202,0.0001066211,0.0000385896,0.00006667825],"domain_scores_gemma":[0.9993966,0.0003209997,0.00006606954,0.00003507087,0.0001437367,0.00003753803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001201153,0.0008561229,0.0805661,0.0002805726,0.0003866953,0.0002135352,0.0002951862,0.2719126,0.03974093,0.0004295629,0.002985269,0.6011323],"study_design_scores_gemma":[0.00001364546,0.0002057145,0.03221577,0.00004112436,0.00009561398,0.00004976664,0.00009915134,0.9597998,0.006331127,0.0005977997,0.0005319971,0.00001843815],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8680391,0.000779791,0.1270275,0.0002603882,0.0001099435,0.00005710742,0.0004285941,0.0006299841,0.002667789],"genre_scores_gemma":[0.9859245,0.0001452961,0.01225172,0.00005545798,0.00001973734,0.00002655592,0.0004065383,0.00002817688,0.001141985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004537447,"threshold_uncertainty_score":0.009022057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03889294272321766,"score_gpt":0.2613999537842792,"score_spread":0.2225070110610615,"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."}}