{"id":"W2029389635","doi":"10.1145/2207676.2207751","title":"On saliency, affect and focused attention","year":2012,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Helpfulness; Salient; Affect (linguistics); Distraction; User engagement; Cognitive psychology; Psychology; Boosting (machine learning); Computer science; Social psychology; Artificial intelligence; Communication","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":[0.0007468274,0.0004240244,0.0003214666,0.0006826867,0.0002079172,0.0008919317,0.0001743906,0.0004529799,0.002124473],"category_scores_gemma":[0.01093816,0.0002126766,0.0003459113,0.0004497937,0.0003582127,0.0008358291,0.0005268516,0.0003633004,0.000268628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002770651,"about_ca_system_score_gemma":0.0001037809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004894548,"about_ca_topic_score_gemma":0.0004148269,"domain_scores_codex":[0.9993224,0.0002421585,0.00002942519,0.0001463342,0.0001999776,0.00005966411],"domain_scores_gemma":[0.9904439,0.006752269,0.001594401,0.0002467007,0.0005777595,0.0003850474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003305074,0.0009863444,0.3308988,0.001801276,0.0006208723,0.0008814168,0.006981568,0.007838149,0.3512896,0.006160345,0.001802275,0.2874343],"study_design_scores_gemma":[0.00004315457,0.002002376,0.9524157,0.00008318897,0.0002512894,0.0006356983,0.0007122739,0.01603087,0.02040585,0.005024794,0.002318472,0.00007638174],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649273,0.001723883,0.0225443,0.0001862932,0.00003373573,0.00008323381,0.0001460857,0.0001184124,0.01023683],"genre_scores_gemma":[0.9965294,0.0002242755,0.002550984,0.00004917878,0.00003743466,0.00003404179,0.00005714836,0.00001593414,0.0005015795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002124473,"threshold_uncertainty_score":0.007107019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02068178589875666,"score_gpt":0.2783874726327878,"score_spread":0.2577056867340312,"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."}}