{"id":"W2113858633","doi":"10.1145/1026653.1026654","title":"Continuous lifelong capture of personal experience with EyeTap","year":2004,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Argument (complex analysis); Computer science; Mode (computer interface); Human–computer interaction","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":[0.0005776354,0.0002389052,0.0001889721,0.0004563477,0.0005001595,0.00198087,0.0006258794,0.0006681179,0.006022722],"category_scores_gemma":[0.004384012,0.0001991459,0.0002653924,0.0003113093,0.001242519,0.003271351,0.003102617,0.0007669123,0.0007827873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004019283,"about_ca_system_score_gemma":0.0001625083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005902135,"about_ca_topic_score_gemma":0.001067085,"domain_scores_codex":[0.9996405,0.0001203609,0.00001367266,0.00007984083,0.00009502035,0.00005069231],"domain_scores_gemma":[0.9982283,0.000865914,0.0001648574,0.0004600738,0.0001801689,0.0001007737],"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":[0.001182234,0.0002232394,0.01125614,0.0009562807,0.0001221011,0.001684238,0.0790699,0.005263026,0.2535273,0.2308293,0.01220783,0.4036785],"study_design_scores_gemma":[0.0001581165,0.002584762,0.07194936,0.001061721,0.0002762256,0.01006217,0.03409873,0.06843934,0.1975618,0.2088782,0.4044033,0.0005260867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4376379,0.001709366,0.4664567,0.001398728,0.0001566012,0.0002594023,0.0003811734,0.00138266,0.09061746],"genre_scores_gemma":[0.9060428,0.0004685557,0.08154769,0.0002116722,0.00007248727,0.0001623343,0.0001715631,0.0001185294,0.01120435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006022722,"threshold_uncertainty_score":0.02014804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021367395500768,"score_gpt":0.2443050079256117,"score_spread":0.234091333970604,"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."}}