{"id":"W4403616769","doi":"10.5821/dissertation-2117-416263","title":"Objective evaluation on the effectiveness of vergence vision training based on the analysis of eye movements","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vergence (optics); Eye movement; Training (meteorology); Artificial intelligence; Computer vision; Computer science; Optometry; Psychology; Physical medicine and rehabilitation; Medicine; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012804,0.0008224989,0.000539357,0.0007881491,0.0002062096,0.0003269238,0.0002924956,0.0004414665,0.004476584],"category_scores_gemma":[0.003152248,0.0001209999,0.0005566236,0.0003097795,0.0002376933,0.000412067,0.0003674911,0.0004443365,0.0004924665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000231294,"about_ca_system_score_gemma":0.0002983044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006574704,"about_ca_topic_score_gemma":0.001104312,"domain_scores_codex":[0.9989421,0.0002305541,0.0001570437,0.0001594832,0.0004234286,0.00008736257],"domain_scores_gemma":[0.9975357,0.0008345884,0.0004715449,0.00006915132,0.0008272799,0.0002617683],"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":[0.0267432,0.01608551,0.1853631,0.007097865,0.001573546,0.0005243521,0.001931619,0.005795659,0.1675496,0.0005771593,0.005542188,0.5812162],"study_design_scores_gemma":[0.001025032,0.07188308,0.845089,0.0004740489,0.001114704,0.0008629644,0.001116201,0.005419656,0.06616466,0.0002577342,0.006434715,0.0001582669],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816653,0.00201026,0.004772902,0.0001301771,0.000124941,0.001019033,0.001509638,0.0001657813,0.008601991],"genre_scores_gemma":[0.9866711,0.00126724,0.004215722,0.0000757855,0.00005041614,0.0005932671,0.001000113,0.00004072721,0.006085566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004476584,"threshold_uncertainty_score":0.01497567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819695961474217,"score_gpt":0.3381828653401465,"score_spread":0.3099859057254044,"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."}}