{"id":"W4387781159","doi":"10.16910/jemr.14.3.6","title":"Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters","year":2023,"lang":"en","type":"article","venue":"Journal of Eye Movement Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Finite impulse response; Infinite impulse response; Root-raised-cosine filter; Computer science; Low-pass filter; Digital filter; Filter (signal processing); Filter design; Algorithm; Butterworth filter; Prototype filter; Mathematics; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002795288,0.0001960272,0.0003767071,0.000694997,0.0003410934,0.001148089,0.003023315,0.0000909572,0.0000228712],"category_scores_gemma":[0.0004117484,0.0001745375,0.00006037244,0.0007189706,0.0001917268,0.001752838,0.003016587,0.001044049,0.00004561243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001129867,"about_ca_system_score_gemma":0.0001144846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001054341,"about_ca_topic_score_gemma":0.00003937635,"domain_scores_codex":[0.9966982,0.0001737861,0.0006113719,0.0005921166,0.001152754,0.0007717485],"domain_scores_gemma":[0.9975558,0.000500685,0.0002437134,0.001106519,0.0003444913,0.0002488125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001904543,0.0008328407,0.6051637,0.0001598309,0.0005449592,0.002620871,0.002125779,0.001136567,0.0804073,0.004594512,0.017772,0.2844512],"study_design_scores_gemma":[0.001664847,0.001019755,0.8252112,0.0006165344,0.00002408778,0.00002239378,0.001328345,0.1440944,0.005035945,0.01225564,0.008181414,0.0005454364],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666491,0.0001957091,0.02929374,0.003005374,0.0002981563,0.0001254142,0.0000382874,0.000142365,0.0002518643],"genre_scores_gemma":[0.9958254,0.0001362989,0.00349786,0.00008322952,0.0002518272,0.000003186432,0.0000353641,0.00002084339,0.0001459921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2839057,"threshold_uncertainty_score":0.9998888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1802622865671849,"score_gpt":0.4320153818137153,"score_spread":0.2517530952465303,"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."}}