{"id":"W1985491065","doi":"10.1117/12.2003730","title":"Monitoring cells in engineered tissues with optical coherence phase microscopy: Optical phase fluctuations as endogenous sources of contrast","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Engineering and Physical Sciences Research Council; Fonds Québécois de la Recherche sur la Nature et les Technologies; Canadian Institutes of Health Research; Research Councils UK","keywords":"Microscopy; Confocal microscopy; Materials science; Optical coherence tomography; Microscope; Phase contrast microscopy; Coherence (philosophical gambling strategy); Optical microscope; Phase (matter); Biomedical engineering; Optics; Biological system; Scanning electron microscope; Chemistry; Physics; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003585834,0.0002896169,0.0001618863,0.0003679728,0.0001433169,0.0004024802,0.0002311961,0.0002987361,0.0003311365],"category_scores_gemma":[0.0005188141,0.0001473504,0.00008589812,0.0003614351,0.0003376757,0.0005627472,0.0003088768,0.0003775994,0.00009629266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003538791,"about_ca_system_score_gemma":0.0003018565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009719664,"about_ca_topic_score_gemma":0.001671356,"domain_scores_codex":[0.9998329,0.00003630403,0.000007557659,0.00003231384,0.00006923379,0.00002172177],"domain_scores_gemma":[0.9997091,0.0001449773,0.0000606372,0.00001895165,0.00005029336,0.00001601564],"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":[0.00004535191,0.00001131179,0.0006559265,0.00003704753,0.000002899386,0.0000259827,0.00002902276,0.0007907221,0.9909974,0.0002742857,0.00004583607,0.007084135],"study_design_scores_gemma":[0.00001045388,0.0000916983,0.003545207,0.000007008423,0.00001211356,0.0001433674,0.00003475148,0.02359604,0.9712105,0.0002506967,0.001086398,0.00001169878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8064953,0.002225484,0.1887713,0.0002906552,0.00003376618,0.00005699182,0.0001943054,0.000234215,0.001697917],"genre_scores_gemma":[0.8823203,0.001669541,0.1142429,0.0001169395,0.00002375648,0.00009730062,0.0001661297,0.00006525407,0.001297826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009719664,"threshold_uncertainty_score":0.00256753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01332497201265525,"score_gpt":0.2490082087734837,"score_spread":0.2356832367608284,"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."}}