{"id":"W4313270883","doi":"10.1371/journal.pone.0276726","title":"SPECHT: Self-tuning Plausibility based object detection Enables quantification of Conflict in Heterogeneous multi-scale microscopy","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"National Institute on Aging; Fondation Brain Canada; Natural Sciences and Engineering Research Council of Canada; British Columbia Knowledge Development Fund; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"STED microscopy; Microscopy; Computer science; Artificial intelligence; Pattern recognition (psychology); Object (grammar); Resolution (logic); Computer vision; Physics; Image (mathematics); Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003760422,0.0001054165,0.000188841,0.0001109504,0.00007837846,0.0000112754,0.0001527057,0.00006766323,0.00003910246],"category_scores_gemma":[0.0000589701,0.0001272409,0.00007830418,0.0001854964,0.00003872421,0.000003727929,0.0001001817,0.0001123351,0.000001039655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006888276,"about_ca_system_score_gemma":0.00004233791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002055288,"about_ca_topic_score_gemma":0.0004880476,"domain_scores_codex":[0.9988808,0.0001617787,0.0002839664,0.0003627955,0.0001662713,0.0001443816],"domain_scores_gemma":[0.9992655,0.00001583557,0.0001502968,0.0004601625,0.00008163039,0.00002660314],"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":[0.0000829528,0.001388609,0.007838588,0.0000547549,0.00006238744,0.000001717902,0.00005783638,0.0002737047,0.9901058,1.390817e-7,0.000007833643,0.0001256814],"study_design_scores_gemma":[0.0003365193,0.0002280403,0.001124483,0.00001171349,0.00005954324,0.000001525316,0.00004924276,0.01377918,0.9841003,0.000002943436,0.0001872356,0.0001192725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888207,0.0002446432,0.01051048,0.00001173058,0.000006807912,0.0003016856,0.0000184733,0.00003893592,0.00004651837],"genre_scores_gemma":[0.987826,0.0000530133,0.01174289,0.00004623224,0.00001646002,0.00009142273,0.0001440441,0.00001845668,0.00006144477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01350548,"threshold_uncertainty_score":0.5188734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02994767379000195,"score_gpt":0.2670682405668376,"score_spread":0.2371205667768357,"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."}}