{"id":"W2887113898","doi":"10.1117/12.2280209","title":"Quantitative compressional OCE: obviating pitfalls in using pre-calibrated compliant layers and some other practical obstacles","year":2018,"lang":"en","type":"article","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Curvature; Stiffness; Nonlinear system; Modulus; Resolution (logic); Realization (probability); Materials science; Biomedical engineering; Computer science; Acoustics; Mathematics; Physics; Composite material; Artificial intelligence; Statistics; Engineering; Geometry","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":[0.007634338,0.001083499,0.0006892912,0.001349258,0.0004636503,0.001370767,0.001145741,0.00124524,0.002227436],"category_scores_gemma":[0.01545546,0.0005784659,0.0002323253,0.0009183576,0.001938939,0.00247279,0.001842488,0.001479312,0.0005763505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004018484,"about_ca_system_score_gemma":0.0006304666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006651905,"about_ca_topic_score_gemma":0.001086892,"domain_scores_codex":[0.9972216,0.0009402806,0.000150074,0.0003615782,0.001168499,0.0001580825],"domain_scores_gemma":[0.9881372,0.006920983,0.0008684248,0.002226108,0.001673849,0.000173486],"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":[0.0005115655,0.0001003534,0.006180164,0.001799026,0.00008828714,0.0005717262,0.0009146866,0.006242211,0.7633101,0.01857874,0.001310253,0.200393],"study_design_scores_gemma":[0.00004471765,0.0006465294,0.009293869,0.0002955117,0.0001271923,0.002491218,0.0007010939,0.05108631,0.9044836,0.0190107,0.01165358,0.0001655604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07642169,0.004935759,0.9104457,0.001023351,0.0001817398,0.0002476202,0.000199541,0.001393964,0.005150581],"genre_scores_gemma":[0.4443833,0.002175415,0.5508373,0.0003308984,0.0001268279,0.0001799877,0.0001742399,0.0003267609,0.001465426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007634338,"threshold_uncertainty_score":0.04037476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06458531760126655,"score_gpt":0.3446798573571213,"score_spread":0.2800945397558547,"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."}}