{"id":"W3209076702","doi":"10.16995/glossa.5886","title":"Towards a complete Logical Phonology model of intrasegmental changes","year":2021,"lang":"en","type":"article","venue":"Glossa a journal of general linguistics","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Unification; Feature (linguistics); Phonology; Voice; Variety (cybernetics); Linguistics; Set (abstract data type); Computer science; Concatenation (mathematics); Reciprocal; Artificial intelligence; Mathematics; Speech recognition; Arithmetic; 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":[0.002286289,0.0006776416,0.0007810825,0.001728315,0.001173711,0.004462479,0.003631074,0.002496501,0.01187348],"category_scores_gemma":[0.001992446,0.0008921097,0.001967469,0.0008152169,0.007687476,0.01259499,0.002671424,0.003844112,0.00444936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001509415,"about_ca_system_score_gemma":0.00143733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001263298,"about_ca_topic_score_gemma":0.001384068,"domain_scores_codex":[0.9992294,0.0002000755,0.00005112488,0.0001561525,0.0002805836,0.00008260849],"domain_scores_gemma":[0.998963,0.0002578794,0.0000935523,0.0003444044,0.000251305,0.00008978262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007751131,0.00001006354,0.00007936847,0.00002710708,0.000006450135,0.00009994601,0.0001237224,0.001891637,0.0006975118,0.9926059,0.0009094388,0.003541039],"study_design_scores_gemma":[0.000009031097,0.000009025983,0.0000975231,0.000009638357,0.000004249032,0.0000582116,0.0000369447,0.007783521,0.0001659337,0.9876646,0.004154637,0.00000673536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02573141,0.002071812,0.8050621,0.01710076,0.000679079,0.000164838,0.0006555887,0.001063615,0.1474708],"genre_scores_gemma":[0.5466003,0.002769409,0.3825023,0.004746957,0.001660997,0.0007666309,0.001069543,0.0007455362,0.05913831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01187348,"threshold_uncertainty_score":0.03972077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09918001637287548,"score_gpt":0.3668710476079664,"score_spread":0.2676910312350909,"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."}}