A georeferenced rRNA amplicon database of aquatic microbiomes from South America
Bibliographic record
Abstract
The biogeography of bacterial communities is a key topic in Microbial Ecology. Regarding continental water, most studies are carried out in the northern hemisphere, leaving a gap on microorganism's diversity patterns on a global scale. South America harbours approximately one third of the world's total freshwater resources, and is one of these understudied regions. To fill this gap, we compiled 16S rRNA amplicon sequencing data of microbial communities across South America continental water ecosystems, presenting the first database µSudAqua[db]. The database contains over 866 georeferenced samples from 9 different ecoregions with contextual environmental information. For its integration and validation we constructed a curated database (µSudAqua[db.sp]) using samples sequenced by Illumina MiSeq platform with commonly used prokaryote universal primers. This comprised ~60% of the total georeferenced samples of the µSudAqua[db]. This compilation was carried out in the scope of the µSudAqua collaborative network and represents one of the most complete databases of continental water microbial communities from South America.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 0.001 |
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; both teacher heads agree on what is shown here.
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".