{"id":"W4405273443","doi":"10.3758/s13428-024-02517-x","title":"The PSR corpus: A Persian sentence reading corpus of eye movements","year":2024,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Reading (process); Sentence; Eye movement; Persian; Computer science; Natural language processing; Artificial intelligence; Linguistics; Philosophy","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":[0.000833353,0.001167221,0.0005581314,0.003854605,0.0007317237,0.000943483,0.000872666,0.001000533,0.0165498],"category_scores_gemma":[0.004664044,0.0002389464,0.000379267,0.003094014,0.0004817119,0.0007979886,0.001099627,0.0007652168,0.01031773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005417338,"about_ca_system_score_gemma":0.0009460845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006785161,"about_ca_topic_score_gemma":0.01119948,"domain_scores_codex":[0.9991615,0.0002309644,0.0001554832,0.0002490367,0.0001570154,0.00004611792],"domain_scores_gemma":[0.9974457,0.001065299,0.0002297333,0.0004380264,0.00074141,0.0000799325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009417737,0.0004853858,0.01411196,0.01143421,0.0002382741,0.003431396,0.006481342,0.001681547,0.04130825,0.004587614,0.5157639,0.3995344],"study_design_scores_gemma":[0.0002848005,0.000210052,0.1672469,0.0008396797,0.0001514569,0.003377572,0.002667027,0.002409377,0.0133691,0.002581141,0.8067271,0.0001357862],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1476745,0.01274847,0.01421851,0.000875254,0.0008499648,0.001098122,0.7774085,0.007091844,0.03803483],"genre_scores_gemma":[0.1558441,0.002696849,0.0289706,0.0004196836,0.0003136178,0.002139017,0.7974476,0.001162033,0.01100652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0165498,"threshold_uncertainty_score":0.05536461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1968168578204524,"score_gpt":0.5401224052394038,"score_spread":0.3433055474189514,"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."}}