{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005197363,0.0001647813,0.0001397879,0.00008300792,0.001514855,0.00116182,0.002324739,0.00006327203,0.001356487],"category_scores_gemma":[0.0009853962,0.0001355122,0.00004663956,0.00107406,0.0001378659,0.0009044551,0.002109275,0.0002617499,0.003090298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005638558,"about_ca_system_score_gemma":0.000003400751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.598176e-7,"about_ca_topic_score_gemma":2.936284e-8,"domain_scores_codex":[0.9976635,0.0003961226,0.000284595,0.0008726265,0.0004008216,0.0003823124],"domain_scores_gemma":[0.998039,0.00005342542,0.0001519131,0.001204678,0.0002368364,0.0003141245],"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":[0.00006421072,0.00005938986,0.000008024429,0.00002275614,0.00001375678,0.00003674378,0.000999922,0.001389061,0.007550921,0.002547602,0.8480211,0.1392864],"study_design_scores_gemma":[0.0003704045,0.0003238696,0.0003803775,0.00001580363,0.000004659683,0.00006480255,0.00004552635,0.02422295,0.001161032,0.0004676962,0.9727187,0.0002242222],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001114846,0.00005501833,0.9798324,0.01315906,0.0001033794,0.0005726603,0.0003467416,0.001627204,0.00318876],"genre_scores_gemma":[0.8971556,0.0001713046,0.08085594,0.004989297,0.0007292805,3.907489e-7,0.01274908,0.00252791,0.0008211904],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8989764,"threshold_uncertainty_score":0.9998751,"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."}}