{"id":"W7115274031","doi":"","title":"Bitext Lexical Dataset - Language Variants - French","year":2023,"lang":"fr","type":"article","venue":"The COCOON platform (University of Paris)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Complement (music); Vocabulary; Variation (astronomy); French; Lexical item; Component (thermodynamics)","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":[0.001191572,0.003435383,0.001651511,0.006498291,0.001525223,0.002631296,0.002269997,0.002922373,0.04908806],"category_scores_gemma":[0.005409219,0.0007983909,0.001910088,0.005341186,0.0006943177,0.001912457,0.002304599,0.002102725,0.05270688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255721,"about_ca_system_score_gemma":0.001516502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02504291,"about_ca_topic_score_gemma":0.03317748,"domain_scores_codex":[0.9978164,0.0003934371,0.0002462253,0.0006485108,0.0006305352,0.0002648367],"domain_scores_gemma":[0.9980596,0.0005496197,0.000114202,0.0005558861,0.0005938078,0.0001268811],"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":[0.0005041095,0.0002440927,0.003872574,0.001226906,0.000161764,0.0006892548,0.0001530897,0.001529683,0.003116973,0.002370992,0.9544489,0.03168181],"study_design_scores_gemma":[0.0009491919,0.0002151812,0.02194754,0.0004733484,0.0001480234,0.002067151,0.0005152529,0.007296616,0.004281782,0.002689742,0.9592759,0.000140193],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009104397,0.0005296658,0.001409813,0.0002063112,0.000159929,0.0001446953,0.9742239,0.007888023,0.006333436],"genre_scores_gemma":[0.004365973,0.0000722996,0.001519664,0.00005996443,0.00001348188,0.0001262074,0.9924971,0.0002728741,0.001072455],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04908806,"threshold_uncertainty_score":0.1642159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03726691051004186,"score_gpt":0.2708516701176232,"score_spread":0.2335847596075814,"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."}}