Bacterioplankton dispersal and biogeochemical function across Alaskan Arctic catchments
Bibliographic record
Abstract
Abstract In Arctic catchments, bacterioplankton are dispersed through soils and streams, both of which freeze and thaw/flow in phase, seasonally. To characterize this dispersal and its potential impact on biogeochemistry, we collected bacterioplankton and measured stream physicochemistry during snowmelt and after vegetation senescence across multiple stream orders in alpine, tundra, and tundra‐dominated‐by‐lakes catchments. In all catchments, differences in community composition were associated with seasonal thaw, then attachment status (i.e. free floating or sediment associated), and then stream order. Bacterioplankton taxonomic diversity and richness were elevated in sediment‐associated fractions and in higher‐order reaches during snowmelt. Families Chthonomonadaceae , Pyrinomonadaceae , and Xiphinematobacteraceae were abundantly different across seasons, while Flavobacteriaceae and Microscillaceae were abundantly different between free‐floating and sediment‐associated fractions. Physicochemical data suggested there was high iron (Fe + ) production (alpine catchment); Fe + production and chloride (Cl − ) removal (tundra catchment); and phosphorus (SRP) removal and ammonium (NH 4 + ) production (lake catchment). In tundra landscapes, these ‘hot spots’ of Fe + production and Cl − removal accompanied shifts in species richness, while SRP promoted the antecedent community. Our findings suggest that freshet increases bacterial dispersal from headwater catchments to receiving catchments, where bacterioplankton‐mineral relations stabilized communities in free‐flowing reaches, but bacterioplankton‐nutrient relations stabilized those punctuated by lakes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".