{"id":"W2038020252","doi":"10.1016/s0968-4328(03)00039-8","title":"Image analysis of tendon helical superstructure using interference and polarized light microscopy","year":2003,"lang":"en","type":"article","venue":"Micron","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":138,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Bayer Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Polarized light microscopy; Materials science; Polarizer; Birefringence; Superstructure; Microscopy; Optics; Crimp; Interference (communication); Planar; Differential interference contrast microscopy; Anatomy; Composite material; Physics; Channel (broadcasting); Computer science; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001173252,0.00009746446,0.0002141593,0.0001496747,0.00006993193,0.00007897842,0.00006845274,0.00006727945,0.001046988],"category_scores_gemma":[0.00003491744,0.00008728555,0.0000531414,0.0003358737,0.0000562189,0.0001508142,0.00002491441,0.00005396217,0.000007602231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003623257,"about_ca_system_score_gemma":0.00002611905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000639859,"about_ca_topic_score_gemma":0.00002934238,"domain_scores_codex":[0.9992853,0.00007919174,0.0002076207,0.0002169512,0.00007757062,0.0001333179],"domain_scores_gemma":[0.9996297,0.00001525421,0.00009538834,0.000150428,0.00006350074,0.00004578002],"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":[0.00003471006,0.00002187472,0.001551809,0.00001401674,0.00002238745,0.000001263894,0.0003398205,9.960294e-7,0.997919,0.00002870679,0.00001119012,0.00005420274],"study_design_scores_gemma":[0.0002121269,0.00002045513,0.005294745,0.00001086294,0.0001616479,0.00001336051,0.0001313372,0.0003437147,0.9930731,0.00000965598,0.000636217,0.00009279884],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915647,0.00008474711,0.007923985,0.0000546166,0.0001473838,0.00007102396,0.00003837256,0.00001721983,0.00009799038],"genre_scores_gemma":[0.9931509,0.00001515718,0.006662773,0.0000596428,0.00001302373,9.362215e-7,0.00001497535,0.000007105945,0.00007550133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004845944,"threshold_uncertainty_score":0.9998662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096660515440245,"score_gpt":0.2652450438874882,"score_spread":0.2542784387330857,"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."}}