{"id":"W4398427745","doi":"10.7910/dvn/itvlik/hcco5b","title":"pre-merge-commit.sample","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Commit; Merge (version control); Computer science; Database; Parallel 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002031227,0.000368996,0.0004138198,0.0001837765,0.0001493233,0.0005776458,0.004083224,0.0001982341,0.007867024],"category_scores_gemma":[0.0006164779,0.0003721849,0.000137398,0.0005984319,0.00006342988,0.0008702242,0.002482222,0.0003985349,0.2013468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004754022,"about_ca_system_score_gemma":0.000217443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000301039,"about_ca_topic_score_gemma":0.00007894553,"domain_scores_codex":[0.9976307,0.0001151882,0.0004449892,0.0008340063,0.0006020293,0.0003731173],"domain_scores_gemma":[0.9963517,0.0001311915,0.0002723404,0.002832194,0.0000863926,0.0003261625],"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":[0.000004254681,0.00006926907,0.000003053333,0.00008302768,0.00005149379,0.00006384481,0.0000257341,0.00000977637,0.000001376491,0.001949845,0.9973487,0.00038961],"study_design_scores_gemma":[0.0002453632,0.00004275526,0.000005778199,0.00003885955,0.00005775411,0.000006472802,0.00000821641,0.007103376,0.00001157863,0.0001208745,0.9919372,0.0004218043],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[6.78438e-8,6.741752e-7,0.1477403,0.00004142519,0.0007333527,0.0001391329,0.8510979,0.000176119,0.00007106017],"genre_scores_gemma":[8.868618e-7,0.0003511251,0.006762807,0.003271217,0.0002715114,0.00001010113,0.9890698,0.00001800615,0.0002445615],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1934798,"threshold_uncertainty_score":0.999873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02492523784114841,"score_gpt":0.2811614299723452,"score_spread":0.2562361921311968,"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."}}