{"id":"W4311327061","doi":"10.1007/978-1-0716-2811-9_10","title":"Optical Tissue Clearing Enables Three-Dimensional Morphometry in Experimental Nerve Regeneration Research","year":2022,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mental Health Research Canada","funders":"","keywords":"Computer science; Segmentation; Clearing; Regeneration (biology); Key (lock); Biomedical engineering; Artificial intelligence; Computer vision; Neuroscience; Biology; Engineering; Cell biology","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":[0.001390529,0.0006549606,0.0004864837,0.001258708,0.001066399,0.001671966,0.000883523,0.0009145195,0.003125321],"category_scores_gemma":[0.001209651,0.001235956,0.0004524633,0.0007612616,0.002178256,0.001781991,0.001201064,0.002105231,0.0008076052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008171261,"about_ca_system_score_gemma":0.001330734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002192167,"about_ca_topic_score_gemma":0.004612422,"domain_scores_codex":[0.9992317,0.000116564,0.00004923099,0.0001728104,0.0003247386,0.0001050226],"domain_scores_gemma":[0.9980958,0.000779337,0.0003112267,0.0005503876,0.0001650608,0.00009815725],"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":[0.00009638932,0.00004945484,0.0004669334,0.0001150795,0.000007866853,0.0001087769,0.0001045497,0.0005808669,0.9759626,0.007555504,0.0002858984,0.01466621],"study_design_scores_gemma":[0.00002476384,0.0001091526,0.005071787,0.00003554191,0.00002213502,0.0008397173,0.00009087283,0.006921778,0.9743536,0.001959269,0.01052247,0.00004885229],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3056986,0.006469152,0.672215,0.000923422,0.0005219728,0.0003772826,0.0005486608,0.001732737,0.0115132],"genre_scores_gemma":[0.5523633,0.005371199,0.4300285,0.0003379326,0.0001308376,0.0004224263,0.0004820245,0.0008390782,0.01002471],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003125321,"threshold_uncertainty_score":0.01045519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05597309689180675,"score_gpt":0.4376954255716668,"score_spread":0.3817223286798601,"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."}}