{"id":"W2202853334","doi":"","title":"Using Crowd-sourcing to Improve the Semantic Transparency of Committee-Designed Languages","year":2014,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Open Source Software Innovations","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Transparency (behavior); Notation; Semantics (computer science); Documentation; Visualization; Interoperability; Programming language; Workflow; Human–computer interaction; Software engineering; Natural language processing; Artificial intelligence; World Wide Web; Linguistics; Database","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"],"consensus_categories":[],"category_scores_codex":[0.0069373,0.0003739771,0.0005077291,0.0003042818,0.0005046885,0.0006506083,0.00459563,0.0002276257,0.00003070892],"category_scores_gemma":[0.001605458,0.0003389241,0.0002269563,0.00103303,0.0002230543,0.0002030778,0.002404586,0.0006739193,0.00002429844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001072546,"about_ca_system_score_gemma":0.000333139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002132529,"about_ca_topic_score_gemma":0.0006044555,"domain_scores_codex":[0.9932259,0.003999169,0.0008639592,0.0008684025,0.0006136053,0.0004289217],"domain_scores_gemma":[0.9902341,0.002149818,0.0007893019,0.004445717,0.002236205,0.0001448965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002597698,0.001562748,0.008191806,0.001471396,0.0005487078,0.00001858175,0.1621169,0.01308435,0.1350768,0.5008211,0.002653948,0.1744277],"study_design_scores_gemma":[0.001755235,0.000005847811,0.01371983,0.009861686,0.000334305,0.00005104197,0.00121818,0.4672455,0.4720811,0.0216454,0.009209258,0.00287266],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09053829,0.0002163258,0.892,0.01091191,0.000251846,0.0007228316,0.00003076493,0.0002668428,0.005061165],"genre_scores_gemma":[0.7510321,0.00001214825,0.2477804,0.0002246961,0.00002586286,0.0000475514,0.00003161622,0.00003794376,0.0008076213],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6604938,"threshold_uncertainty_score":0.9999063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075150141007998,"score_gpt":0.2630002434995157,"score_spread":0.2422487420894358,"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."}}