{"id":"W4285267073","doi":"10.1109/jiot.2022.3181607","title":"PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Social Science Fund of China; National Natural Science Foundation of China","keywords":"Computer science; Pupil; Morphology (biology); Computer network","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004120199,0.0007323581,0.0009403259,0.001289181,0.0002816034,0.0006411558,0.001010208,0.0007202218,0.003019247],"category_scores_gemma":[0.001659407,0.0005040118,0.0007143368,0.001032486,0.0001719428,0.001151676,0.0006205986,0.0005134394,0.002163185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002935194,"about_ca_system_score_gemma":0.0004029759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006409042,"about_ca_topic_score_gemma":0.01531089,"domain_scores_codex":[0.9996299,0.00004588622,0.00002160598,0.0001216711,0.0001482599,0.00003278089],"domain_scores_gemma":[0.9993349,0.0001584366,0.00007458349,0.0001263639,0.0002641223,0.00004156719],"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":[0.0004378043,0.000302416,0.008579059,0.0002861593,0.0001770785,0.0002565609,0.0001984409,0.02878265,0.05702057,0.0009879998,0.01262065,0.8903506],"study_design_scores_gemma":[0.00005053313,0.000298985,0.0112823,0.00002090109,0.00006948736,0.0002974129,0.00008002196,0.961421,0.01903966,0.0008687971,0.006514024,0.00005696724],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08703192,0.001245429,0.8885684,0.0002034712,0.0001025945,0.0002391781,0.001070807,0.01687217,0.004666048],"genre_scores_gemma":[0.4194884,0.0008367005,0.5693038,0.0003013847,0.00008617077,0.0002083737,0.001990443,0.0005405608,0.007244083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006409042,"threshold_uncertainty_score":0.01274347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04470289827238697,"score_gpt":0.2795395881361712,"score_spread":0.2348366898637843,"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."}}