{"id":"W1489121383","doi":"10.1007/11840930_52","title":"Feature Conjunctions in Visual Search","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Visual search; Conjunction (astronomy); Feature (linguistics); Task (project management); Artificial intelligence; Object (grammar); Motion (physics); Pattern recognition (psychology); Information retrieval","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008300449,0.0004367265,0.0004184868,0.00166415,0.0002692426,0.0005430482,0.001715789,0.0004171055,0.00002652954],"category_scores_gemma":[0.00003687454,0.000413866,0.0001432667,0.001381598,0.0004606986,0.0006416138,0.0007227219,0.001278422,0.000108894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004459376,"about_ca_system_score_gemma":0.000426495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008437419,"about_ca_topic_score_gemma":0.0004244097,"domain_scores_codex":[0.9963112,0.00005946761,0.0004025896,0.001452349,0.001099578,0.0006747966],"domain_scores_gemma":[0.9985524,0.0001752896,0.0001405549,0.0007677415,0.0002261547,0.0001378944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001626214,0.0002046642,0.0007421077,0.00007060995,0.00001324006,0.0002766428,0.0006292925,0.08224201,0.0008735637,0.03156859,0.0005599602,0.882803],"study_design_scores_gemma":[0.0006573051,0.0004257976,0.003547255,0.0003703262,0.000006353469,0.0001479042,5.066873e-7,0.9463569,0.001472812,0.0394455,0.006535879,0.001033497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005051502,0.0001635014,0.9886376,0.001638907,0.002180472,0.0003493079,0.000002879488,0.0001910907,0.006331055],"genre_scores_gemma":[0.8581653,0.00005896157,0.1275881,0.002904535,0.001210889,0.00003228483,0.0000290107,0.00008459784,0.009926363],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8817695,"threshold_uncertainty_score":0.9998313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01842780742137592,"score_gpt":0.283624217303832,"score_spread":0.2651964098824561,"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."}}