Correction: Corrigendum: Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification
Bibliographic record
Abstract
Scientific Reports 5: Article number: 16498; Published online: 13 November 2015; Updated: 02 June 2016 The Acknowledgements section in this Article is incomplete. “This work was supported by the European Research Council (FP7 ERC-Synergy Nexus1492 project grant number 319209) and the US National Institutes of Health (R01 GM089886), the BBVA Foundation (I Ayudas a Investigadores, Innovadores y Creadores), the Generalitat Valenciana (VALi+d APOSTD/2014/123 and GV/2015/060), the European Union (EUROTAST FP7 PEOPLE-2010 MC ITN, Braudel-IFER-FMSH; FP7/2007-2013 - MSCA-COFUND, n°245743), and the Centre for Chronic Diseases and Disorders (C2D2) Research Priming Fund grant to CFS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.053 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".