{"id":"W4407941965","doi":"10.1145/3689050.3704428","title":"E-Serging: Exploring the Use of Overlockers (Sergers) in Creating E-Textile Seams and Interactive Yarns for Garment Making, Embroidery, and Weaving","year":2025,"lang":"en","type":"article","venue":"","topic":"Crafts, Textile, and Design","field":"Arts and Humanities","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weaving; Textile; Computer science; Clothing; Yarn; Engineering drawing; Engineering; Materials science; Mechanical engineering; Composite material","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":[0.001110433,0.0006593997,0.0001782762,0.000664108,0.001187668,0.002288204,0.0009307878,0.001034434,0.004707179],"category_scores_gemma":[0.00189005,0.0003002838,0.0004000022,0.0004737502,0.002689164,0.002882174,0.002290852,0.0007424948,0.0007421432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003068156,"about_ca_system_score_gemma":0.0003228068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004358661,"about_ca_topic_score_gemma":0.002400854,"domain_scores_codex":[0.9994804,0.0002643228,0.00001495665,0.00007456897,0.00009716477,0.0000684882],"domain_scores_gemma":[0.9990937,0.000585577,0.00005635739,0.0001532632,0.00004744727,0.00006375504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007703785,0.0009580469,0.01858772,0.00325363,0.00006531375,0.004653291,0.1802083,0.005202853,0.2166617,0.06322739,0.009520015,0.4968913],"study_design_scores_gemma":[0.0001707895,0.002961649,0.0512695,0.001965507,0.0002270422,0.01262465,0.2148817,0.02538768,0.1392123,0.03658321,0.5143439,0.0003721164],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8239256,0.0009787519,0.1061586,0.000624819,0.00009754328,0.0002449594,0.0001042418,0.0005512965,0.0673143],"genre_scores_gemma":[0.8506867,0.0008429407,0.1259906,0.0002638819,0.00001501127,0.0001417722,0.00007000721,0.0002268156,0.02176219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004707179,"threshold_uncertainty_score":0.01574713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1212898238169291,"score_gpt":0.2887300862257475,"score_spread":0.1674402624088184,"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."}}