{"id":"W3216795403","doi":"10.32920/ryerson.14658024.v1","title":"Overload Detection/Health Monitoring Landing Gear Sensor System Proposal","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Landing gear; Aisle; Flight plan; Service (business); Aeronautics; Engineering; Automotive engineering; Plan (archaeology); Computer science; Marine engineering; Real-time computing; Aerospace 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":[0.0007413671,0.0004297803,0.0005299639,0.0009059903,0.0002801904,0.0009491208,0.0008968418,0.0007952948,0.005048027],"category_scores_gemma":[0.0006867992,0.0002083874,0.0002259851,0.0002747555,0.0001881903,0.0007517496,0.0004567839,0.0004902079,0.002417007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004748314,"about_ca_system_score_gemma":0.0005318779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009237464,"about_ca_topic_score_gemma":0.0008643516,"domain_scores_codex":[0.9994975,0.00005924176,0.00003723209,0.0001107941,0.0002499216,0.00004526128],"domain_scores_gemma":[0.9996197,0.00004673582,0.00002670977,0.00004536834,0.0002312624,0.00003016513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001165524,0.0003680093,0.01178448,0.0009090997,0.00008836933,0.0005740635,0.0004134295,0.0131845,0.3550777,0.00795911,0.03184018,0.5766355],"study_design_scores_gemma":[0.0003464122,0.00440657,0.03012986,0.0002832698,0.0002291854,0.002018691,0.0004437031,0.3297189,0.4377894,0.005575029,0.1887731,0.0002858565],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.1495022,0.001704778,0.7804039,0.00153225,0.0009531799,0.002328282,0.003159181,0.02848055,0.03193568],"genre_scores_gemma":[0.6270224,0.001236538,0.3174379,0.001345119,0.0003744194,0.001312116,0.005303262,0.0002680238,0.0457002],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.005048027,"threshold_uncertainty_score":0.01688737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008500522187583084,"score_gpt":0.2148274149958297,"score_spread":0.2063268928082467,"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."}}