{"id":"W4414569139","doi":"10.1016/j.measurement.2025.119052","title":"Corrigendum to “Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures” [Measurement 257(Part A) (2026) 118620]","year":2025,"lang":"en","type":"article","venue":"Measurement","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Contamination; Fermentation; Mie scattering; Dynamic light scattering","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":[0.002579038,0.002676633,0.003425265,0.004073468,0.003833474,0.002809989,0.004205174,0.007066318,0.1255453],"category_scores_gemma":[0.01889061,0.001511648,0.003509045,0.002417896,0.001427819,0.002065094,0.00264466,0.005944033,0.07231488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005178283,"about_ca_system_score_gemma":0.003779428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0370795,"about_ca_topic_score_gemma":0.07279124,"domain_scores_codex":[0.9957676,0.0003950535,0.0003964534,0.0007567944,0.002262001,0.0004220867],"domain_scores_gemma":[0.9821713,0.001737456,0.0004461928,0.001016329,0.01370699,0.0009216869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002832399,0.00001721829,0.00005392569,0.0000749465,0.00001654987,0.00009438233,0.000009974024,0.00008100866,0.0005205402,0.0003370689,0.9928402,0.00592593],"study_design_scores_gemma":[0.00003984573,0.00004968666,0.002521331,0.00007810639,0.00007991525,0.0002524392,0.00003716596,0.00108102,0.003101016,0.001345695,0.9913297,0.00008420712],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0002540381,0.002090671,0.002109652,0.03616211,0.9498127,0.00008722847,0.0009437341,0.0007799102,0.007759999],"genre_scores_gemma":[0.008924775,0.006990658,0.005661733,0.06713598,0.2346607,0.0002601727,0.004442922,0.001441544,0.6704814],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1255453,"threshold_uncertainty_score":0.419991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0355143556860052,"score_gpt":0.2726700308667018,"score_spread":0.2371556751806966,"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."}}