Hydrography shapes bacterial biogeography of the deep Arctic Ocean
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It has been long debated as to whether marine microorganisms have a ubiquitous distribution or patterns of biogeography, but recently a consensus for the existence of microbial biogeography is emerging. However, the factors controlling the distribution of marine bacteria remain poorly understood. In this study, we combine pyrosequencing and traditional Sanger sequencing of the 16S rRNA gene to describe in detail bacterial communities from the deep Arctic Ocean. We targeted three separate water masses, from three oceanic basins and show that bacteria in the Arctic Ocean have a biogeography. The biogeographical distribution of bacteria was explained by the hydrography of the Arctic Ocean and subsequent circulation of its water masses. Overall, this first taxonomic description of deep Arctic bacteria communities revealed an abundant presence of SAR11 (Alphaproteobacteria), SAR406, SAR202 (Chloroflexi) and SAR324 (Deltaproteobacteria) clusters. Within each cluster, the abundance of specific phylotypes significantly varied among water masses. Water masses probably act as physical barriers limiting the dispersal and controlling the diversity of bacteria in the ocean. Consequently, marine microbial biogeography involves more than geographical distances, as it is also dynamically associated with oceanic processes. Our ocean scale study suggests that it is essential to consider the coupling between microbial and physical oceanography to fully understand the diversity and function of marine microbes.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it