{"id":"W2112296121","doi":"10.7557/12.3411","title":"Reductio ad discrimen: Where features come from","year":2015,"lang":"en","type":"article","venue":"Nordlyd","topic":"Historical Linguistics and Language Studies","field":"Arts and Humanities","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Linguistics; Construct (python library); Phonology; Universal grammar; Grammar; Contrast (vision); Set (abstract data type); Variety (cybernetics); Variation (astronomy); Construction grammar; Reductio ad absurdum; Computer science; Philosophy; Interpretation (philosophy); Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000487929,0.0001087012,0.0001582047,0.00002599092,0.0002151456,0.00009058286,0.0001133889,0.0000284222,0.001752868],"category_scores_gemma":[0.0001132303,0.00008421325,0.00004913354,0.00001854276,0.0001380535,0.00004164957,0.00004618955,0.0001206701,0.0002701655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005244464,"about_ca_system_score_gemma":0.0000216504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004075173,"about_ca_topic_score_gemma":0.006716377,"domain_scores_codex":[0.9993752,0.00001907408,0.0001190963,0.0001625806,0.0001678592,0.0001562126],"domain_scores_gemma":[0.9995298,0.00003122665,0.00003885413,0.0001717249,0.0001320959,0.00009628327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002778801,0.00008103647,0.0001023332,0.00001000175,0.00006635698,0.00003162396,0.09295507,0.000001279407,0.00003381555,0.1330888,0.7707632,0.002838718],"study_design_scores_gemma":[0.0001805057,0.00005151654,0.0001098277,0.00001139239,0.00002497973,5.949637e-7,0.007470498,0.000002549081,0.0000155107,0.004523322,0.9874753,0.0001339426],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.009437189,0.01418022,0.00001263468,0.001230584,0.004560171,0.00009054215,0.00020242,0.0001273441,0.9701589],"genre_scores_gemma":[0.8601421,0.00003572378,0.0003080936,0.0002829094,0.003767656,0.00001038408,0.00005506696,0.00002076065,0.1353773],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.850705,"threshold_uncertainty_score":0.9991597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04107996255906895,"score_gpt":0.2548399513390308,"score_spread":0.2137599887799618,"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."}}