Nitrogen Inputs by Associative Cyanobacteria across a Low Arctic Tundra Landscape
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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 it