{"id":"W2095019168","doi":"10.1007/s11554-010-0178-1","title":"A real-time framework for eye detection and tracking","year":2010,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"BitTorrent tracker; Computer science; Robustness (evolution); Eye tracking; Artificial intelligence; Overtaking; Computer vision; Boosting (machine learning); Machine learning; Engineering","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":[0.0009422713,0.0001923145,0.0003627152,0.0003101786,0.0003156845,0.0005088836,0.0005321048,0.0002183392,0.000008813813],"category_scores_gemma":[0.0006311267,0.0001661104,0.000111879,0.0003490199,0.0001501449,0.001199227,0.00008493569,0.0006668266,0.000008745168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000036892,"about_ca_system_score_gemma":0.0001345123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005171063,"about_ca_topic_score_gemma":0.000001146648,"domain_scores_codex":[0.9985769,0.00004051834,0.00047729,0.0003028841,0.0002513795,0.0003510051],"domain_scores_gemma":[0.9980431,0.0002617059,0.0007576675,0.000244204,0.000565881,0.0001274016],"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":[0.00002388387,0.0000389509,0.0001512509,0.00004758193,0.00001253533,0.00002107892,0.0001707494,0.00000154748,0.6726511,0.0002435486,0.00004809193,0.3265897],"study_design_scores_gemma":[0.003697488,0.002122212,0.04547641,0.002257538,0.0003357185,0.004163018,0.000264266,0.1917692,0.6040878,0.1412688,0.002777354,0.001780289],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3452241,0.0000741138,0.6526409,0.001026294,0.0002082674,0.00007991934,0.000001122904,0.0001905713,0.0005546274],"genre_scores_gemma":[0.4737312,0.00003690431,0.5258581,0.00002311295,0.0002237451,0.000003778617,2.107693e-7,0.00001979361,0.0001031676],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3248094,"threshold_uncertainty_score":0.6773787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038162542398621,"score_gpt":0.2853600160224005,"score_spread":0.2749783905984143,"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."}}