{"id":"W4401232833","doi":"10.1038/s41598-024-68597-z","title":"Precision improvement of robotic bioprinting via vision-based tool path compensation","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Computer science; Compensation (psychology); Path (computing); Artificial intelligence; Computer vision; Computer network","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":[0.003063609,0.0001209458,0.0001586793,0.0003535374,0.00009248409,0.0003010436,0.0001554544,0.00007260681,0.0002402915],"category_scores_gemma":[0.00036407,0.0001047331,0.00009191725,0.0008050996,0.00015673,0.0001277575,0.0001019193,0.0001854696,0.00006419564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001195848,"about_ca_system_score_gemma":0.0001113833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001410833,"about_ca_topic_score_gemma":0.000001601731,"domain_scores_codex":[0.9974695,0.0000205983,0.0006574298,0.0004895627,0.001057585,0.0003052817],"domain_scores_gemma":[0.9988838,0.0001743603,0.00006933575,0.0006360991,0.0001433662,0.00009299521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001796442,0.00003467144,0.0006010004,0.0005261308,0.00001643423,0.0001058615,0.00007178807,0.0217761,0.806191,0.00003774453,0.001841472,0.168796],"study_design_scores_gemma":[0.00005742954,0.00004150603,0.001934871,0.000494168,0.000009619032,0.00001414055,0.00000887345,0.706511,0.2838751,0.002224004,0.004687741,0.0001415092],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8214619,0.0002772718,0.1657439,0.00008363479,0.01067025,0.0004646888,0.000001421818,0.000439757,0.0008571264],"genre_scores_gemma":[0.9958605,0.000002041165,0.003731971,0.000003413769,0.00004963706,0.00001552168,0.00002548068,0.00002498857,0.0002864864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6847349,"threshold_uncertainty_score":0.427089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241183895254718,"score_gpt":0.2774131571937319,"score_spread":0.2650013182411848,"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."}}