Alder (<i>Alnus crispa</i>) effects on soils in ecosystems of the Agashashok River valley, northwest Alaska
Bibliographic record
Abstract
At the northern limit of the boreal forest biome, alder (Alnus crispa [Ait.] Pursh) shrubs occur in a variety of ecosystems. We assessed the effects of individual alder shrubs on soil properties and understory plant tissue nitrogen in floodplain terraces, valley slopes and tussock tundra ridges. The three ecosystems differed with respect to soil properties and abiotic conditions and supported distinct plant communities. Alder increased resin-exchangeable soil N and NO3 production significantly in each ecosystem. The greatest difference between alder canopy and surrounding soil NO3 measured both under field and laboratory conditions occurred in floodplain sites. The shrub effect on soil pH and soil organic matter was greatest on tundra ridges. Alder shrubs also influenced the nitrogen nutrition of plants growing beneath their canopies. Plants growing below alder canopies had higher foliar nitrogen concentration and natural abundance 15N composition and lower carbon to nitrogen ratio than open-grown plants. Similar to soil N availability, understory plant leaf chemistry responded more to alder on floodplains than on slope or tundra ecosystems. This pattern suggests that understory plants rely more heavily on alder-fixed-N in this resource-poor ecosystem.
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 | 0.001 | 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".