{"id":"W2028264897","doi":"10.1117/12.527092","title":"A focus of attention mechanism for gaze control within a framework for intelligent image analysis tools","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Focus (optics); Computer science; Computer vision; Object (grammar); Segmentation; Gaze; Image segmentation; Cluster analysis; Image (mathematics); Object detection; Field (mathematics); Mathematics","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":[0.0009993474,0.0006417563,0.0005035326,0.0008429997,0.0007720708,0.001666147,0.001637168,0.001061391,0.004109506],"category_scores_gemma":[0.002755279,0.0003709941,0.0006877456,0.0003388029,0.001466507,0.001938639,0.001097092,0.0009035131,0.0009030901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008983052,"about_ca_system_score_gemma":0.0009095424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00306343,"about_ca_topic_score_gemma":0.002642496,"domain_scores_codex":[0.9994727,0.00009779271,0.00002808445,0.0001667268,0.0001772571,0.00005743769],"domain_scores_gemma":[0.9992481,0.0002543869,0.00008529424,0.0001494628,0.0001878893,0.00007489147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004272381,0.0002866986,0.001537874,0.0002901198,0.0001570096,0.0006872125,0.002451821,0.02826303,0.3391984,0.3059375,0.009523842,0.3112393],"study_design_scores_gemma":[0.0002050083,0.00075237,0.004773784,0.0001544407,0.0002033588,0.001035695,0.0003076973,0.6933899,0.1011289,0.1285237,0.06929705,0.0002281646],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008207145,0.0001932611,0.9848003,0.0002431817,0.00006698994,0.0001330669,0.00002708482,0.002379186,0.003949798],"genre_scores_gemma":[0.3696181,0.0002695791,0.6218715,0.00024428,0.0001387558,0.0004412669,0.00005952824,0.0002475794,0.007109472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004109506,"threshold_uncertainty_score":0.01374763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348796836394159,"score_gpt":0.2452016289906936,"score_spread":0.231713660626752,"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."}}