{"id":"W1989462718","doi":"10.1037/a0023851","title":"An amorphous model for morphological processing in visual comprehension based on naive discriminative learning.","year":2011,"lang":"en","type":"article","venue":"Psychological Review","topic":"Reading and Literacy Development","field":"Psychology","cited_by":594,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Ministry of Science and Environmental Protection","keywords":"Phrase; Discriminative model; Lexical decision task; Inflection; Natural language processing; Artificial intelligence; Linguistics; Computer science; Reading (process); Word (group theory); Psychology; Cognitive psychology; Speech recognition; Cognition","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":[0.0009656433,0.0003479028,0.0006552666,0.0001308498,0.0001222961,0.00002375354,0.0003519961,0.0002205702,0.0008730568],"category_scores_gemma":[0.0002516602,0.0002345668,0.0001646889,0.0003048209,0.0001389772,0.00009001695,0.00002767771,0.0005529668,0.0001766113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006180224,"about_ca_system_score_gemma":0.00001856814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008921305,"about_ca_topic_score_gemma":8.826554e-7,"domain_scores_codex":[0.9969982,0.0005896806,0.0006541582,0.000976836,0.0002347251,0.0005464026],"domain_scores_gemma":[0.9989437,0.0001862578,0.0002365322,0.000341534,0.0001037455,0.0001882071],"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":[0.002735296,0.01125654,0.008504376,0.001066507,0.00002664158,0.0004734926,0.007057912,0.0006117899,0.0002864996,0.003143225,0.004589354,0.9602484],"study_design_scores_gemma":[0.008861578,0.01838322,0.5050581,0.01358378,0.0002763597,0.0002539342,0.0009416103,0.4216214,0.00007145564,0.007564359,0.02010669,0.003277611],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7310573,0.02085974,0.1770181,0.00138145,0.0009213897,0.006289439,0.00003497241,0.0008432441,0.06159442],"genre_scores_gemma":[0.9786652,0.0006157737,0.0135985,0.005872938,0.00003588305,0.0007470192,0.00006543543,0.00003370848,0.0003654895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9569708,"threshold_uncertainty_score":0.9565356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1687323487128092,"score_gpt":0.4443724441424893,"score_spread":0.2756400954296802,"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."}}