{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002440651,0.00006885661,0.00005780171,0.00007272454,0.00009546367,0.0000654047,0.000109086,0.0000323311,0.00004514142],"category_scores_gemma":[0.00001743456,0.00005399773,0.00003372873,0.0001453575,0.00001449367,0.000533039,0.00005277995,0.00004930713,0.0002914279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001446068,"about_ca_system_score_gemma":0.000003403397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008163514,"about_ca_topic_score_gemma":0.000001987482,"domain_scores_codex":[0.9993743,0.00005171604,0.0000847556,0.0001575786,0.0001520182,0.0001795993],"domain_scores_gemma":[0.999673,0.00003117558,0.0000271336,0.0001653753,0.00001516951,0.00008812116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000007765046,0.000234248,0.01436698,0.00001165211,0.000009159636,9.287099e-7,0.0002824723,0.000002054083,0.007850507,0.8339009,0.00179587,0.1415375],"study_design_scores_gemma":[0.001385406,0.001065793,0.9435538,0.00003561293,0.00001416468,0.00006384312,0.00006796969,0.02541594,0.01125103,0.01218668,0.004351296,0.0006084726],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.595487,0.00002531837,0.3922282,0.0003089849,0.0005594567,0.00008420255,1.335649e-7,0.0002067954,0.01109985],"genre_scores_gemma":[0.9966502,0.000003776211,0.002074694,0.0003126467,0.00003998653,0.000006424177,4.686348e-7,0.000003334992,0.0009084503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9291868,"threshold_uncertainty_score":0.3745814,"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."}}