{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001865143,0.0001390109,0.0002048922,0.0001442182,0.000269684,0.0002698138,0.00006801597,0.00002144592,0.0001474686],"category_scores_gemma":[0.00009994055,0.0001020702,0.00005135622,0.00004001683,0.0001187179,0.000454046,0.00009151479,0.00009976498,5.805484e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003688533,"about_ca_system_score_gemma":0.00002279693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008002563,"about_ca_topic_score_gemma":0.002174725,"domain_scores_codex":[0.9991781,0.00003639235,0.0002738981,0.0002237656,0.00008443456,0.0002034213],"domain_scores_gemma":[0.9988654,0.0008346192,0.00008607724,0.0001340813,0.00005556887,0.00002426287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007984044,0.0003258924,0.01431172,0.0008125781,0.000444285,0.00001316363,0.4562386,0.0007260088,0.002185122,0.4212631,0.01714742,0.08573373],"study_design_scores_gemma":[0.00298754,0.0004475675,0.01266138,0.002212912,0.0002223916,0.000006948626,0.5037981,0.05151247,0.003379144,0.004710261,0.4171962,0.0008650303],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981095,0.000198743,0.001095526,0.0002588528,0.0003105582,0.0005254241,0.000013661,0.00004129849,0.016461],"genre_scores_gemma":[0.9901249,0.00005252533,0.0003580163,0.0002309401,0.0000634547,0.00009242831,0.000002111474,0.00001329898,0.009062279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4165528,"threshold_uncertainty_score":0.4162302,"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."}}