{"id":"W6912526499","doi":"10.5281/zenodo.3590121","title":"Saner 2020: Cross-Dataset Design Discussion Mining: Replication Package","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Python (programming language); Download; R package; Directory; Replication (statistics); JavaScript; Package design; Root (linguistics)","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.02808236,0.002320937,0.001789751,0.002746077,0.001780994,0.004566612,0.003963179,0.001935396,0.3104756],"category_scores_gemma":[0.1053796,0.002879046,0.003654993,0.002568616,0.0010669,0.004886662,0.004671974,0.004060379,0.2127143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316422,"about_ca_system_score_gemma":0.00586125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002734134,"about_ca_topic_score_gemma":0.006561725,"domain_scores_codex":[0.989446,0.005130001,0.0008938766,0.00231463,0.001743998,0.0004715005],"domain_scores_gemma":[0.9406721,0.02899894,0.002077756,0.0171023,0.009262467,0.001886409],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002945754,0.00003705104,0.0006353853,0.0007711548,0.0001569974,0.00002317452,0.000228214,0.0004341104,0.0006234412,0.002285324,0.9796503,0.01486036],"study_design_scores_gemma":[0.00111722,0.00007787276,0.002066794,0.0003749389,0.0001627735,0.00006392713,0.0001685676,0.004099308,0.003970547,0.01912145,0.9686397,0.0001368134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002242579,0.0003282098,0.1642554,0.003467354,0.001933607,0.00307075,0.5022265,0.2979277,0.02454805],"genre_scores_gemma":[0.01497339,0.0003103568,0.2940235,0.004414486,0.0006172859,0.02682413,0.3719333,0.2537453,0.03315825],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9719176,"threshold_uncertainty_score":0.9835229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0638414889590741,"score_gpt":0.2887634634651938,"score_spread":0.2249219745061197,"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."}}