{"id":"W6955169463","doi":"10.57745/gd2cqu","title":"pp_and_macros.zip","year":2025,"lang":"en","type":"dataset","venue":"Recherche Data Gouv France","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Macro; Artifact (error); Term (time); Process (computing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00182047,0.003443781,0.0024417,0.004353947,0.001632944,0.004258487,0.004088899,0.002489131,0.326258],"category_scores_gemma":[0.008221372,0.001560414,0.001929453,0.007847587,0.0008009211,0.002864421,0.003077477,0.002621086,0.3889242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002160889,"about_ca_system_score_gemma":0.003134015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0190926,"about_ca_topic_score_gemma":0.0291266,"domain_scores_codex":[0.9983833,0.0002475598,0.0001561849,0.0006219211,0.0002848608,0.0003063358],"domain_scores_gemma":[0.9957067,0.001289946,0.0003433321,0.001253462,0.0009269997,0.0004796299],"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":[0.00005421305,0.00001062144,0.0003089534,0.0003948355,0.00001904264,0.000007019769,0.00001318502,0.00009142447,0.00008571279,0.0002893598,0.9977111,0.001014472],"study_design_scores_gemma":[0.0003088323,0.00001705628,0.001939738,0.0001960416,0.00002909333,0.00002835339,0.00005152205,0.0002148282,0.0005743057,0.001796778,0.9948048,0.00003866577],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002398496,0.00001640949,0.00003879194,0.00002741215,0.00001447489,0.000005963728,0.998924,0.000534166,0.000414752],"genre_scores_gemma":[0.0001788231,0.00002993514,0.0002483416,0.00006068185,0.000008468076,0.00007038466,0.9982084,0.0004553164,0.0007396741],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.673742,"threshold_uncertainty_score":0.9610112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3281710609002049,"score_gpt":0.4639312754663376,"score_spread":0.1357602145661327,"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."}}