{"id":"W4411307290","doi":"10.21606/drs.2014.74","title":"Designing Affiliative Objects: Investigating the Affiliations of Medical Identification Jewellery","year":2014,"lang":"en","type":"article","venue":"Proceedings of DRS","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Human–computer interaction; Computer science; Psychology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02495266,0.0007223216,0.0003654727,0.001958358,0.007939451,0.006971261,0.002330278,0.002211248,0.004843424],"category_scores_gemma":[0.03543102,0.0007676284,0.0005178262,0.001051249,0.01332504,0.006467034,0.007941536,0.001285711,0.0006659013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006733958,"about_ca_system_score_gemma":0.007237747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004185689,"about_ca_topic_score_gemma":0.008476422,"domain_scores_codex":[0.9697267,0.02554889,0.0005413765,0.001080822,0.00184548,0.001256788],"domain_scores_gemma":[0.9667914,0.02335117,0.003196421,0.002511187,0.002202355,0.001947464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002714829,0.0005614407,0.03488968,0.0008623258,0.00002655773,0.001076085,0.8413575,0.0005482887,0.007931,0.05254401,0.001426774,0.05850484],"study_design_scores_gemma":[0.0001426856,0.001963392,0.0335453,0.00072379,0.00009156472,0.0008640306,0.8290175,0.001970283,0.008966303,0.01378626,0.108784,0.0001448787],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8924408,0.0002611555,0.06315618,0.001503293,0.00008271046,0.001256287,0.00004760691,0.0001469047,0.04110508],"genre_scores_gemma":[0.9458227,0.0001378162,0.04773649,0.0002876126,0.00001121994,0.001329262,0.00004185398,0.00004843899,0.004584527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02495266,"threshold_uncertainty_score":0.1319639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02111414065737422,"score_gpt":0.2778950558662477,"score_spread":0.2567809152088735,"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."}}