{"id":"W4392561808","doi":"10.7554/elife.88591.2.sa2","title":"eLife Assessment: MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Mechanics and Biomechanics Studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Toolbox; Python (programming language); Differentiable function; Artificial neural network; Computer science; Artificial intelligence; Effector; Deep neural networks; Programming language; Mathematics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007234651,0.001093159,0.001907701,0.0002885211,0.0001815596,0.0003418036,0.0004495337,0.000630953,0.0002269241],"category_scores_gemma":[0.00004112357,0.0007730988,0.0006298113,0.0005522891,0.0000261539,0.000071081,0.0001617285,0.0009779813,0.00002986383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002134909,"about_ca_system_score_gemma":0.00006234959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002736131,"about_ca_topic_score_gemma":0.0001565113,"domain_scores_codex":[0.9962415,0.00006146443,0.0009504814,0.0009831501,0.0006780789,0.001085328],"domain_scores_gemma":[0.99844,0.0003361936,0.000158703,0.0005861893,0.0002409646,0.0002379782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009804784,0.0001151331,4.898014e-7,0.01669615,0.002533854,0.00003889266,0.00001155406,0.003127482,0.00183237,0.01123547,0.9461687,0.01814183],"study_design_scores_gemma":[0.0004069252,0.000403525,1.654759e-7,0.002200496,0.001099339,0.000007595897,0.000006701784,0.737345,0.000238694,0.0005353516,0.2569542,0.0008019809],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001186975,0.06922854,0.8991114,0.003789286,0.01763117,0.005714186,0.001375326,0.00182742,0.001203926],"genre_scores_gemma":[0.3954844,0.1079345,0.0375745,0.01234256,0.04573862,0.03280598,0.03383056,0.008089771,0.326199],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.861537,"threshold_uncertainty_score":0.999472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335194340523253,"score_gpt":0.2763322807943253,"score_spread":0.2529803373890928,"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."}}