{"id":"W4298213377","doi":"10.5167/uzh-107991","title":"Voyages dans le temps et dans l’espace d’un mot marin : batture. Ce qu’en dévoilent les mises en relief métalinguistiques dans les grands corpus","year":2014,"lang":"fr","type":"preprint","venue":"Zurich Open Repository and Archive (University of Zurich)","topic":"Linguistics and Discourse Analysis","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008560561,0.0003725299,0.000189175,0.001428316,0.002901997,0.002405893,0.0004203273,0.0006497143,0.01296556],"category_scores_gemma":[0.002374888,0.0002911696,0.0001901325,0.00249227,0.002183006,0.001745659,0.00128639,0.001092119,0.001654145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003161858,"about_ca_system_score_gemma":0.003218333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1801258,"about_ca_topic_score_gemma":0.4208392,"domain_scores_codex":[0.9994783,0.0001444964,0.00002885445,0.0001548799,0.0001441083,0.00004954008],"domain_scores_gemma":[0.9991912,0.0003007078,0.00007703277,0.00009211925,0.0002890331,0.00004977462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005024167,0.00005090464,0.04911393,0.001565927,0.0000798431,0.00173458,0.5133346,0.0008362308,0.03765806,0.0900926,0.03353281,0.2714981],"study_design_scores_gemma":[0.00001535437,0.0000610061,0.1153698,0.0006949368,0.00005035041,0.001067854,0.1339522,0.001104949,0.006084661,0.004210813,0.7373114,0.00007657595],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7214939,0.005228236,0.02503874,0.006262361,0.00115356,0.0002386134,0.007160361,0.0004421255,0.2329821],"genre_scores_gemma":[0.8798268,0.001503684,0.01912252,0.0004176851,0.00008262211,0.0001707566,0.002915327,0.0003480159,0.0956126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1801258,"threshold_uncertainty_score":0.3581547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01433763180851592,"score_gpt":0.230483159739238,"score_spread":0.2161455279307221,"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."}}