Nitrogen fixation by associative cyanobacteria in the Canadian Arctic.
Bibliographic record
Abstract
Atmospheric N\u2082-fixation by cyanobacteria is a key source of newly fixed N in nutrient-poor arctic ecosystems. To further determine the causes of N limitation and predict long-term responses to climate change the controls of biological N\u2082-fixation must be better understood. Using acetylene reduction assays we evaluated the spatial and temporal variation in N\u2082-fixation by associative cyanobacteria in various ecosystem types in both the low and high Canadian Arctic. The direct and indirect effects of soil moisture, plant community functional composition, and bryophyte and lichen abundance on rates of N\u2082-fixation were examined at sites varying in latitude and vegetation type. The linkages between N and C cycling processes in arctic systems were examined through paired measurements of N\u2082-fixation, inorganic soil N with surface greenhouse gas fluxes, including CO\u2082, N\u2082O and CH\u2084. Total growing season N\u2082-fixation input across a low arctic landscape was estimated at 0.68 kg ha\u207b~yr\u207b~, which is slightly less than twice the estimated average N input 0.39 kg ha\u207b~yr\u207b~ via precipitation. N\u2082-fixation by bryophyte-cyanobacterial associations appear to be very important across the Canadian Arctic. Increasing soil moisture was strongly associated with an increasing presence of bryophytes and increasing bryophyte abundance was a major factor determining higher N\u2082-fixation rates at all sites. Shrubs had a negative effect on bryophyte abundance; competition from vascular plants, potentially through shading, may negatively influence N\u2082-fixation. Soil N status was linked to rates or N\u2082-fixation in both the high and low Arctic indicating that these N\u2082-fixing associations act as important point sources of soil N. Higher rates of nitrification may be associated with warmer and drier vegetation types; however, increasing NO\u2083-N availability does not appear to increase rates of denitrification. Loss of N through denitrification was not a significant factor in the N cycle at the high arctic sites examined. We found many factors control both the spatial and temporal variability of N\u2082-fixation, including topography, microtopography, vegetation characteristics, microclimatic conditions, nifH abundance and availability of other nutrients, such as phosphorus. Moisture, however, appears to be a key factor not only in determining N\u2082-fixation but also by influencing related nutrient cycling processes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".