{"id":"W4386796678","doi":"10.23977/jeis.2023.080303","title":"Intelligent Algorithm Evaluation of Incidental English Vocabulary Acquisition in Complex Reading Tasks","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocabulary; Computer science; Reading (process); Class (philosophy); Perspective (graphical); Mathematics education; Foreign language; Extensive reading; Artificial intelligence; Psychology; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00555563,0.00005436989,0.0001152412,0.0008915621,0.00007676762,0.00006040741,0.000138161,0.0000373832,0.0009884363],"category_scores_gemma":[0.0002600123,0.00004981874,0.00003056512,0.0009859143,0.00007557526,0.002084482,0.00002901195,0.0001814335,0.00001477759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000158045,"about_ca_system_score_gemma":0.0001805543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009795152,"about_ca_topic_score_gemma":9.636003e-7,"domain_scores_codex":[0.9985191,0.00008132883,0.0005343499,0.00006424663,0.0006151085,0.0001858591],"domain_scores_gemma":[0.9986705,0.00005170248,0.0004509893,0.00007887921,0.0006971251,0.00005079472],"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":[0.000120528,0.00008880756,0.001820646,0.00002743718,0.00003631167,0.00001118661,0.08907703,0.004250333,0.01024352,0.01718721,0.001415578,0.8757214],"study_design_scores_gemma":[0.003976133,0.001270108,0.5225347,0.0001699205,0.00006751591,0.0004808858,0.08112483,0.3682519,0.005829106,0.001853941,0.01406032,0.0003806929],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935799,0.0006782374,0.001999965,0.00006522102,0.0002816446,0.00009997287,0.00000233439,0.000008995175,0.003283791],"genre_scores_gemma":[0.9991754,0.0001001301,0.0002169101,0.0004264698,0.00005285058,0.000002129182,0.00001730159,0.000002249201,0.000006534294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8753407,"threshold_uncertainty_score":0.9999248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02853772488313189,"score_gpt":0.3523044243088161,"score_spread":0.3237666994256843,"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."}}