{"id":"W4293232299","doi":"10.1075/lia.21003.bel","title":"L’acquisition des objets directs et indirects en français L1","year":2022,"lang":"fr","type":"article","venue":"Language Interaction and Acquisition","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Humanities; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008333962,0.0003316862,0.0002830424,0.0003949107,0.0007372847,0.0006290893,0.0003884113,0.0001647626,0.002352365],"category_scores_gemma":[0.00009357325,0.0003709989,0.0001218726,0.0006595266,0.0001201126,0.002521709,0.0006221397,0.0008514156,0.00008100789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004027086,"about_ca_system_score_gemma":0.00006228759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002077086,"about_ca_topic_score_gemma":0.00008090738,"domain_scores_codex":[0.9971197,0.0008896831,0.0003730089,0.0006699539,0.0004999897,0.0004477047],"domain_scores_gemma":[0.9988021,0.0002697421,0.0002704505,0.000367027,0.0001396307,0.0001510319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002602552,0.0006802217,0.00051469,0.0005159529,0.0001160228,0.001127692,0.154162,0.0001326849,0.07818025,0.03257053,0.006548507,0.7251912],"study_design_scores_gemma":[0.007397098,0.00531318,0.04407348,0.006523807,0.0006999319,0.02053983,0.07075586,0.08993866,0.4738884,0.1289861,0.1446592,0.007224504],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7595403,0.1115668,0.1021069,0.01102208,0.005590481,0.0007864627,0.0001792529,0.001816645,0.007391129],"genre_scores_gemma":[0.9623621,0.0003231372,0.02861878,0.004629559,0.0003805273,0.0001013381,0.0001565625,0.00004025637,0.003387757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7179667,"threshold_uncertainty_score":0.9998742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399384440175484,"score_gpt":0.2960243486677767,"score_spread":0.2820305042660219,"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."}}