{"id":"W2089031095","doi":"10.1080/10255840902740620","title":"The use of artificial neural networks to reduce data collection demands in determining spine loading: a laboratory based analysis","year":2009,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics & Biomedical Engineering","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Australian Government; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Inverse dynamics; Joint (building); Artificial neural network; Reduction (mathematics); Computer science; Compression (physics); Work (physics); Structural engineering; Statistics; Simulation; Engineering; Mathematics; Machine learning; Mechanical engineering; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002821428,0.0001854882,0.0004906027,0.001266897,0.00004634291,0.00003664603,0.0002663547,0.0001868032,0.000003197929],"category_scores_gemma":[0.001161965,0.0001471096,0.0001072186,0.005639021,0.00004240516,0.00009117691,0.0001161166,0.0003206542,2.085646e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001231858,"about_ca_system_score_gemma":0.00006160552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001215118,"about_ca_topic_score_gemma":0.000006206169,"domain_scores_codex":[0.9979017,0.0002786333,0.0007599497,0.0004336688,0.0002833029,0.0003427394],"domain_scores_gemma":[0.9982623,0.0007524665,0.00009897023,0.0006139014,0.00007783809,0.0001945214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001312632,0.0002574576,0.0001357836,0.0001503891,0.0001086075,0.00001927038,0.0001946666,0.07060614,0.3464892,0.00008661896,0.00008419807,0.5817364],"study_design_scores_gemma":[0.0004314986,0.0004209884,0.00699155,0.0001928518,0.00006978292,0.000001623093,0.00001169248,0.9896957,0.00152617,0.00001639422,0.000501852,0.0001398899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09158134,0.000106516,0.9067799,0.0006029161,0.0005501048,0.0003271808,0.000003302378,0.00004827539,5.093768e-7],"genre_scores_gemma":[0.4163897,0.00001178562,0.583127,0.0002283718,0.0001641087,0.00001123364,0.00005180148,0.00001412335,0.000001889692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9190896,"threshold_uncertainty_score":0.5998955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05333111359689729,"score_gpt":0.3670098887001055,"score_spread":0.3136787751032082,"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."}}