{"id":"W2903960665","doi":"10.1109/smartworld.2018.00183","title":"A Comparison of Inertial Data Acquisition Methods for a Position-Independent Soil Types Recognition","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Wearable computer; Computer science; Wearable technology; Mobile phone; Reliability (semiconductor); Data acquisition; Work (physics); Inertial frame of reference; Phone; Position (finance); Artificial intelligence; Mobile device; Computer vision; Engineering; Embedded system; Telecommunications","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":[0.001554611,0.0009434363,0.0008557184,0.002982706,0.0002653694,0.0008628828,0.0008809365,0.0008538292,0.002080366],"category_scores_gemma":[0.003559081,0.0003135967,0.0006085281,0.001564345,0.0002389155,0.001152586,0.0005431937,0.0003865556,0.001403384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806973,"about_ca_system_score_gemma":0.0002778449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001616034,"about_ca_topic_score_gemma":0.002352729,"domain_scores_codex":[0.9985909,0.0002521799,0.0001303469,0.0003082368,0.0006242606,0.00009408827],"domain_scores_gemma":[0.9980525,0.0007079596,0.00009395525,0.0002259383,0.0008479481,0.00007164294],"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":[0.00134626,0.0002615016,0.01048639,0.00110948,0.0003677119,0.0001469799,0.0002206422,0.00368373,0.07592359,0.0004896127,0.002405031,0.9035591],"study_design_scores_gemma":[0.0004587176,0.006204209,0.2054306,0.0006065753,0.001479437,0.003544483,0.001623865,0.4263465,0.3048022,0.001711785,0.04725451,0.0005371771],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3194108,0.009984339,0.6537834,0.0003283827,0.001134201,0.0004427786,0.0009797515,0.005402212,0.008534166],"genre_scores_gemma":[0.6056492,0.005882551,0.3787922,0.0002080374,0.00030923,0.0002803146,0.002079643,0.0003580312,0.006440774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002982706,"threshold_uncertainty_score":0.008221626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07181738264281577,"score_gpt":0.3896086819777812,"score_spread":0.3177912993349654,"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."}}