{"id":"W2916601121","doi":"10.4018/ijssci.2018100101","title":"Saliency Priority of Individual Bottom-Up Attributes in Designing Visual Attention Models","year":2018,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Salient; Ranking (information retrieval); Human visual system model; Motion (physics); Perception; Feature (linguistics); Benchmark (surveying); Cognition; Eye tracking; Affect (linguistics); Visual attention; Human motion; Visual search; Machine learning; Computer vision; Cognitive psychology; Image (mathematics); Psychology","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":[0.001336039,0.0007403994,0.0006587266,0.001011156,0.0003329063,0.001056929,0.001125025,0.0006839425,0.001513905],"category_scores_gemma":[0.005298388,0.00031751,0.0006114959,0.0004198438,0.0005244936,0.001757704,0.0007021764,0.0008229316,0.000262697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183569,"about_ca_system_score_gemma":0.0006546111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005422086,"about_ca_topic_score_gemma":0.004440953,"domain_scores_codex":[0.9995555,0.0001323605,0.00002605788,0.00009790338,0.0000950604,0.00009302107],"domain_scores_gemma":[0.9988874,0.0005745185,0.00009372282,0.00007549731,0.000282647,0.00008627633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007202476,0.0003403872,0.008562464,0.0004568631,0.0002226705,0.0002680606,0.0009179125,0.5792345,0.0671434,0.05866221,0.002109617,0.2813617],"study_design_scores_gemma":[0.00001272753,0.0001270049,0.001381266,0.00001302908,0.00003439958,0.00003109979,0.00004089004,0.9857278,0.003213867,0.008977451,0.0004255978,0.00001486124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1066702,0.0004530812,0.8883629,0.0002294884,0.0000567525,0.0001904074,0.00005365671,0.0003988803,0.003584607],"genre_scores_gemma":[0.9340284,0.0001442271,0.06465865,0.00004855449,0.00004021675,0.0001139184,0.00003501263,0.0000307755,0.000900183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005422086,"threshold_uncertainty_score":0.01078105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04315290121628659,"score_gpt":0.3416183788582269,"score_spread":0.2984654776419404,"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."}}