{"id":"W1874608359","doi":"10.1111/jmi.12308","title":"Automated segmentation of wood fibres in micro‐CT images of paper","year":2015,"lang":"en","type":"article","venue":"Journal of Microscopy","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Mitacs","keywords":"Papermaking; Segmentation; Materials science; Fiber; Artificial intelligence; Computer science; Tracking (education); Composite material; Computer vision","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":[0.0006079085,0.0005224182,0.0004143114,0.002068916,0.0004077583,0.001119636,0.0007659824,0.000951246,0.001047014],"category_scores_gemma":[0.001183579,0.0004426242,0.0003905409,0.0009091828,0.0005825432,0.000697955,0.0003616616,0.0005465049,0.0005982218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004277509,"about_ca_system_score_gemma":0.0009481609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001989792,"about_ca_topic_score_gemma":0.005477077,"domain_scores_codex":[0.9996197,0.00003813026,0.00002906793,0.00009611945,0.0001689831,0.00004796744],"domain_scores_gemma":[0.999225,0.0002879746,0.0001089909,0.0001041367,0.0002445482,0.00002934048],"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":[0.0001580273,0.00005641262,0.002278824,0.0002437147,0.00003846999,0.0003247903,0.0002623747,0.01430703,0.8085716,0.001382675,0.0006032129,0.171773],"study_design_scores_gemma":[0.00003235869,0.0001683296,0.02635035,0.00008957538,0.00006506906,0.001702234,0.0002604631,0.2620821,0.6983249,0.002377415,0.008446085,0.0001010113],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.139336,0.0006256458,0.8562203,0.00009530852,0.0000499728,0.0003236382,0.0002766043,0.001563796,0.001508623],"genre_scores_gemma":[0.194269,0.0004416287,0.8032716,0.00004476844,0.00001717223,0.0001358035,0.0003140108,0.0001837203,0.00132238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002068916,"threshold_uncertainty_score":0.003956378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01742911554957724,"score_gpt":0.3170267182583194,"score_spread":0.2995976027087421,"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."}}