Nitrogen Inputs by Associative Cyanobacteria across a Low Arctic Tundra Landscape
Bibliographic record
Abstract
Available soil N is a key factor limiting plant productivity in most low arctic terrestrial ecosystems. Atmospheric N2-fixation by cyanobacteria is often the primary source of newly fixed N in these nutrient-poor environments. We examined temporal and spatial variation in N2-fixation by the principal cyanobacterial associations (biological soil crusts, Sphagnum spp. associations, and Stereocaulon paschale) in a wide range of ecosystems within a Canadian low arctic tundra landscape, and estimated N input via N2-fixation over the growing season using a microclimatically driven model. Moisture and temperature were the main environmental factors influencing N2-fixation. In general, N2-fixation rates were largest at the height of the growing season, although each N2-fixing association had distinct seasonal patterns due to ecosystem differences in microclimatic conditions. Ecosystem types differed strongly in N2-fixation rates with the highest N input (10.89 kg ha−1 yr−1) occurring in low-lying Wet Sedge Meadow and the lowest N input (0.73 kg ha−1 yr−1) in Xerophytic Herb Tundra on upper esker slopes. Total growing season (3 June–13 September) N2-fixation input from measured components across a carefully mapped landscape study area (26.7 km2) was estimated at 0.68 kg ha−1 yr−1, which is approximately twice the estimated average N input via wet deposition. Although biological N2-fixation input rates were small compared to internal soil N cycling rates, our data suggest that cyanobacterial associations may play an important role in determining patterns of plant productivity across low arctic tundra landscapes.
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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 | 0.001 | 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) | 0.000 | 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".