Elevated Atmospheric CO2 and Warming Stimulates Growth and Nitrogen Fixation in a Common Forest Floor Cyanobacterium under Axenic Conditions
Bibliographic record
Abstract
The predominant input of available nitrogen (N) in boreal forest ecosystems originates from moss-associated cyanobacteria, which fix unavailable atmospheric N2, contribute to the soil N pool, and thereby support forest productivity. Alongside climate warming, increases in atmospheric CO2 concentrations are expected in Canada’s boreal region over the next century, yet little is known about the combined effects of these factors on N fixation by forest floor cyanobacteria. Here we assess changes in N fixation in a common forest floor, moss-associated cyanobacterium, Nostoc punctiforme Hariot, under elevated CO2 conditions over 30 days and warming combined with elevated CO2 over 90 days. We measured rates of growth and changes in the number of specialized N2 fixing heterocyst cells, as well as the overall N fixing activity of the cultures. Elevated CO2 stimulated growth and N fixation overall, but this result was influenced by the growth stage of the cyanobacteria, which in turn was influenced by our temperature treatments. Taken together, climate change factors of warming and elevated CO2 are expected to stimulate N2 fixation by moss-associated cyanobacteria in boreal forest systems.
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) | 0.001 | 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".