{"id":"W4366550140","doi":"10.1145/3544548.3581426","title":"Memory Tracer & Memory Compass: Investigating Personal Location Histories as a Design Material for Everyday Reminiscence","year":2023,"lang":"en","type":"article","venue":"","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Reminiscence; Compass; Computer science; Resource (disambiguation); Human–computer interaction; Recall; Process (computing); Overlay; Data science; Psychology; Cognitive psychology; Geography","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":[0.00492886,0.0005497319,0.0002818811,0.001340394,0.003221112,0.007127435,0.001323161,0.001305114,0.004132751],"category_scores_gemma":[0.01307091,0.0005506077,0.000280574,0.0009874983,0.007683397,0.00532115,0.003978667,0.001277221,0.0005669192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002016719,"about_ca_system_score_gemma":0.002351191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003049663,"about_ca_topic_score_gemma":0.007483747,"domain_scores_codex":[0.9965487,0.002553662,0.00008521932,0.0002524919,0.0003958618,0.0001641593],"domain_scores_gemma":[0.9926604,0.005048927,0.0005267066,0.0008893216,0.0004364582,0.0004382057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001981062,0.0001820374,0.009508092,0.0007836239,0.00002527259,0.001085213,0.8804941,0.0003594283,0.005495304,0.04345755,0.004214922,0.05419635],"study_design_scores_gemma":[0.0001135039,0.0007808694,0.01176533,0.0008219538,0.0001049669,0.00191461,0.7100984,0.001496165,0.009913246,0.01928064,0.2435941,0.0001162173],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8707222,0.00210163,0.05335106,0.002975113,0.000126852,0.0007482119,0.0002463285,0.0003586712,0.06936988],"genre_scores_gemma":[0.9658257,0.0007273773,0.02182332,0.0004234712,0.00001243733,0.0005316501,0.00007497886,0.00009296973,0.01048799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007127435,"threshold_uncertainty_score":0.0260666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0831180725621834,"score_gpt":0.3145648124386455,"score_spread":0.2314467398764621,"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."}}