{"id":"W2890886173","doi":"10.23889/ijpds.v3i4.758","title":"What makes great data documentation?","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Manitoba Health","funders":"","keywords":"Documentation; Session (web analytics); Data presentation; Presentation (obstetrics); Resource (disambiguation); Population; Computer science; Data science; Library science; World Wide Web; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.1336342,0.001481061,0.001769833,0.003219073,0.01016737,0.02412725,0.004498709,0.01415044,0.08029309],"category_scores_gemma":[0.2980264,0.001599499,0.002124317,0.00398616,0.009408887,0.03852751,0.01686038,0.02233455,0.04740297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008454337,"about_ca_system_score_gemma":0.02712375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004334535,"about_ca_topic_score_gemma":0.007038532,"domain_scores_codex":[0.8519821,0.09655277,0.009635172,0.009484101,0.02784902,0.00449688],"domain_scores_gemma":[0.6779783,0.1694561,0.01412629,0.02256747,0.07980928,0.03606267],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002621194,0.0000432173,0.0002669012,0.0007672161,0.00001899766,0.0001006828,0.005057985,0.00003828376,0.0002466557,0.006209256,0.9421621,0.04506255],"study_design_scores_gemma":[0.00001848698,0.00002280301,0.0002956758,0.001361745,0.00001078476,0.0001040499,0.005934374,0.00004393484,0.0001935649,0.007087356,0.9848792,0.00004809108],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0005451575,0.004804224,0.009321641,0.905323,0.04255702,0.0005177475,0.0005151558,0.001289518,0.03512649],"genre_scores_gemma":[0.030085,0.01985849,0.0721686,0.7162346,0.058986,0.004160547,0.002824697,0.005372622,0.09030948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9955013,"threshold_uncertainty_score":0.7067333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1801888818686919,"score_gpt":0.5291111611317418,"score_spread":0.34892227926305,"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."}}