{"id":"W4413184332","doi":"10.3791/68529","title":"Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography","year":2025,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Organoid; Optical coherence tomography; Coherence (philosophical gambling strategy); Computer science; Computational biology; Biology; Neuroscience; Physics; Optics","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":[0.0004007056,0.0003265385,0.0002962589,0.0004865071,0.0002498509,0.0005564184,0.0003402723,0.0003857352,0.0008847733],"category_scores_gemma":[0.0006220387,0.0002515911,0.000299606,0.0004482177,0.0003916083,0.0003535006,0.0004335044,0.0007552034,0.0003755277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000530938,"about_ca_system_score_gemma":0.0006320824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001497708,"about_ca_topic_score_gemma":0.00325633,"domain_scores_codex":[0.9998473,0.00002086866,0.00001036932,0.00004248933,0.00006161178,0.0000173208],"domain_scores_gemma":[0.9997258,0.0001192892,0.00003949508,0.00004961902,0.00004682448,0.00001901093],"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":[0.00005968822,0.00002365579,0.0006353969,0.0001122026,0.00001115191,0.0001261018,0.0001128176,0.02602282,0.9455726,0.002445734,0.0004597058,0.02441812],"study_design_scores_gemma":[0.00001028839,0.00007474242,0.00448428,0.00002318207,0.00001147096,0.0001895276,0.00007977973,0.3652045,0.6219301,0.002538158,0.005410641,0.00004330271],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1243714,0.0003006932,0.871689,0.0001520958,0.00001620149,0.00009098469,0.0008217474,0.001196821,0.001361121],"genre_scores_gemma":[0.3440256,0.0006186808,0.6518869,0.00006533806,0.0000119911,0.0004184358,0.001106263,0.000248888,0.001617868],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001497708,"threshold_uncertainty_score":0.003852308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116355853881451,"score_gpt":0.3316569324547112,"score_spread":0.3200213470665661,"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."}}