{"id":"W4406707857","doi":"10.1007/978-3-031-75653-5_9","title":"Enhanced 3D X-Ray Tomography: Deep Learning–Based Advanced Algorithms for Fiber Instance Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Segmentation; Tomography; Artificial intelligence; Computer science; Algorithm; Deep learning; Fiber; X-ray; Computer vision; Materials science; Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001612254,0.0004507701,0.000444271,0.0003725678,0.000102216,0.0000930173,0.00008876689,0.0005599934,0.0006363783],"category_scores_gemma":[0.00001196711,0.0004344831,0.0003328005,0.0001087906,0.00002097538,0.0001254299,0.00001303348,0.0005231894,0.0003614379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001877375,"about_ca_system_score_gemma":0.00002213483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000518455,"about_ca_topic_score_gemma":0.00001575024,"domain_scores_codex":[0.9984546,0.000009463561,0.0005133183,0.0004527238,0.0002931891,0.0002767117],"domain_scores_gemma":[0.9993367,0.0001071762,0.0001140471,0.0002295594,0.0001318037,0.00008076356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001592169,0.00001179188,4.071393e-7,0.001004641,0.0004330988,0.00001155287,0.0001712901,0.298105,0.005279514,0.002778328,0.001916899,0.6901283],"study_design_scores_gemma":[0.001529005,0.0004426367,0.000001894527,0.0008254205,0.0001607526,0.000002639519,0.00006399255,0.105032,0.01331545,0.0007125429,0.8768401,0.001073586],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001612077,0.001670912,0.461342,0.000009603008,0.003890332,0.001842743,0.00007523914,0.001701222,0.5293067],"genre_scores_gemma":[0.02281767,0.000132303,0.01679498,0.00004677951,0.001156826,0.0005145436,0.0003169175,0.0004014139,0.9578186],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8749232,"threshold_uncertainty_score":0.9998107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455823264815989,"score_gpt":0.2410604029821021,"score_spread":0.2265021703339422,"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."}}