{"id":"W1994892719","doi":"10.1145/1773965.1773969","title":"Pilot gaze and glideslope control","year":2008,"lang":"en","type":"article","venue":"ACM Transactions on Applied Perception","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Australian Research Council","keywords":"Runway; Touchdown; Gaze; Cockpit; Fixation (population genetics); Computer science; Flight simulator; Aeronautics; Computer vision; Eye movement; Simulation; Artificial intelligence; Engineering; Medicine; Geography","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":[0.00009727249,0.0001398267,0.0001430129,0.0001558859,0.0003669192,0.00003315234,0.0003749226,0.00006691562,0.00006730636],"category_scores_gemma":[0.000007415316,0.0001351258,0.00003712141,0.0002198387,0.0001377938,0.0001363528,0.000007815064,0.0002536257,0.000233186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004295391,"about_ca_system_score_gemma":0.0000192273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002154913,"about_ca_topic_score_gemma":0.00001156497,"domain_scores_codex":[0.9991014,0.00002550162,0.0001464495,0.0003769031,0.0001464397,0.0002033021],"domain_scores_gemma":[0.9992426,0.00008365406,0.00003662146,0.0005422866,0.00003046412,0.0000643857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001374967,0.0005879159,0.0004571699,0.00001556001,0.00005444355,0.00001716912,0.0012044,0.0006674681,0.1258153,0.01143964,0.0004481881,0.8591552],"study_design_scores_gemma":[0.01310716,0.003635962,0.9008467,0.0001019033,0.0001582735,0.001134651,0.0008172311,0.02044514,0.01800925,0.03086935,0.008459953,0.002414437],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.163816,0.000009342334,0.8332505,0.00109923,0.0001091117,0.0001490298,0.00000319843,0.0004928271,0.001070801],"genre_scores_gemma":[0.9731203,0.00008379487,0.02609186,0.0004493197,0.00002312968,0.00006363452,0.000001427291,0.00001006715,0.0001564985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9003896,"threshold_uncertainty_score":0.5510271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0268054773504891,"score_gpt":0.2352598912502245,"score_spread":0.2084544138997354,"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."}}