{"id":"W4391873011","doi":"10.1101/2024.01.31.578280","title":"Grayscale 4D Biomaterial Customization at High Resolution and Scale","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Materials Research; Real Estate Foundation of British Columbia; University of Washington; Washington Research Foundation; National Institutes of Health; National Science Foundation","keywords":"Grayscale; Nanotechnology; Computer science; Self-healing hydrogels; Photolithography; Materials science; Tissue engineering; Lithography; Biomolecule; Artificial intelligence; Biological system; Image (mathematics); Engineering; Biomedical engineering; Biology; Optoelectronics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001653359,0.0003940561,0.00016636,0.0003055356,0.0001479967,0.0005194306,0.0003194739,0.0002940409,0.002191198],"category_scores_gemma":[0.0002349814,0.0002485105,0.0002325132,0.0001409548,0.0003738204,0.0002794213,0.0005815756,0.000432381,0.0006620999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003811773,"about_ca_system_score_gemma":0.0002046195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004313656,"about_ca_topic_score_gemma":0.001090697,"domain_scores_codex":[0.9999033,0.000008248799,0.000007019056,0.00002263516,0.00003881945,0.00001990997],"domain_scores_gemma":[0.9998432,0.00003838967,0.0000477401,0.00003953209,0.0000161446,0.00001498301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008521004,0.000006582111,0.00009033774,0.00001845779,0.000002674276,0.00003136965,0.00001305474,0.0008720305,0.9961653,0.0003088456,0.0001007961,0.002382067],"study_design_scores_gemma":[0.000003641534,0.00002351273,0.0007523199,0.000003824194,0.000003468644,0.00006950851,0.00001039675,0.006086198,0.9900592,0.0001418682,0.002837979,0.000008206628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8299933,0.0006528738,0.1557164,0.0002743876,0.00009159043,0.0001164788,0.0006754225,0.002223361,0.0102562],"genre_scores_gemma":[0.8761322,0.000478945,0.1171474,0.000150021,0.0000162949,0.0001034025,0.0003170188,0.0004197622,0.005235028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002191198,"threshold_uncertainty_score":0.007330239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008739093895407479,"score_gpt":0.2143659840739833,"score_spread":0.2056268901785759,"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."}}