{"id":"W2038403419","doi":"10.7202/603105ar","title":"Berber Clitic Doubling and Syntactic Extraction","year":2009,"lang":"en","type":"article","venue":"Revue québécoise de linguistique","topic":"Language, Linguistics, Cultural Analysis","field":"Arts and Humanities","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Clitic; Realization (probability); Linguistics; Contrast (vision); Characterization (materials science); Computer science; Natural language processing; Mathematics; Artificial intelligence; Physics; Philosophy; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002521977,0.0002329779,0.0003140438,0.0001322857,0.0003335035,0.0003434972,0.0001316008,0.0001004212,0.0003885798],"category_scores_gemma":[0.001614767,0.000207366,0.0001331565,0.00005788231,0.00009144541,0.0001170732,0.00001804262,0.0003219659,0.00006225678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001420906,"about_ca_system_score_gemma":0.00005843225,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007853438,"about_ca_topic_score_gemma":0.0130065,"domain_scores_codex":[0.9987087,0.0000625535,0.0003858722,0.0003545659,0.0001321611,0.0003561542],"domain_scores_gemma":[0.9989002,0.0001674187,0.0001758865,0.0002629177,0.0003248441,0.0001687091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003589773,0.00009733716,0.0002617287,0.0001346708,0.00007437959,0.0002632757,0.02592614,0.00004103521,0.0005614785,0.9690951,0.0002659657,0.003242965],"study_design_scores_gemma":[0.001088205,0.0004104449,0.003422114,0.0008721864,0.001322455,0.0004842859,0.007428212,0.005053046,0.001063831,0.1585193,0.8183759,0.00195996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5358529,0.00278502,0.0005969389,0.004112824,0.001822359,0.0005228809,0.00005123903,0.0007114515,0.4535444],"genre_scores_gemma":[0.9832669,0.00009919457,0.0008354621,0.0009676223,0.005271501,0.000009854939,0.00002584711,0.00002547239,0.009498135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8181099,"threshold_uncertainty_score":0.9987534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919341256695375,"score_gpt":0.2740492880134672,"score_spread":0.2548558754465134,"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."}}