Nutrients and temperature interact to regulate algae and heterotrophic bacteria in an Alaskan poor fen peatland
Bibliographic record
Abstract
Permafrost thaw associated with warmer temperatures is expected to elevate nutrient levels in northern aquatic ecosystems, including peatlands. To evaluate these effects on algae and heterotrophic bacteria, we manipulated nutrients (nitrogen (N) and phosphorus (P)) and temperature (ambient and warmed) in a factorial design using nutrient diffusing substrates inside warming chambers in an Alaskan peatland. After 16 days, there was no effect of warming on the abundance of algae or heterotrophic bacteria in the absence of nutrient enrichment. Algal production and bacterial biomass were substantially elevated by N with and without P (NP and N, respectively), independent of warming. Warming significantly enhanced the effect of nutrient enrichment on the abundance of algae and heterotrophic bacteria compared with ambient temperatures. Rates of N fixation increased with the presence of heterocyst-forming cyanobacteria, which represented a greater proportion of algal taxonomic composition in the absence of N enrichment in both ambient and warmed conditions. Our results indicate that warmer temperatures and nutrient enrichment will elevate algal and heterotrophic metabolism in northern peatlands, and the magnitude of increase will depend on the combination of nutrients available during periods of inundation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".