{"id":"W6966908484","doi":"10.48448/nxqh-yb30","title":"Quantifying Cognitive Factors in Lexical Decline","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Affect (linguistics); Lexical diversity; Variety (cybernetics); Cognition; Set (abstract data type); Logistic regression; Diversity (politics)","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":[0.002143387,0.0004133705,0.0003352507,0.002828947,0.0004979643,0.001861568,0.0005152534,0.0006569918,0.002609884],"category_scores_gemma":[0.01613935,0.0001836779,0.0003491942,0.001801876,0.001340945,0.001864436,0.001028617,0.0005040543,0.0004322438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005793507,"about_ca_system_score_gemma":0.0003685598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005926151,"about_ca_topic_score_gemma":0.006573055,"domain_scores_codex":[0.9992738,0.0001937987,0.00008711748,0.0001891509,0.0001598527,0.00009622448],"domain_scores_gemma":[0.9900455,0.005027544,0.002917812,0.000546836,0.0009686196,0.0004938006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002098575,0.00009902355,0.9787663,0.0000553756,0.00005587946,0.0001228233,0.001270922,0.001136232,0.003373344,0.0005769235,0.00008827039,0.01424485],"study_design_scores_gemma":[0.000003774038,0.00008649119,0.9942287,0.00001139873,0.0000182367,0.0001384347,0.0009586675,0.001785245,0.0006847868,0.001769993,0.000299998,0.00001426294],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963809,0.0001311389,0.001281255,0.00003308953,0.00000222919,0.00001386856,0.0001691877,0.0000120926,0.001976311],"genre_scores_gemma":[0.9990224,0.00003078137,0.0005044517,0.0000123553,0.000003749516,0.00001346299,0.000210644,0.000006131541,0.0001960151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005926151,"threshold_uncertainty_score":0.01178336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022940409086633,"score_gpt":0.3782774633732299,"score_spread":0.2759834224645666,"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."}}