Leaf Tissue C:N is Modified by Growing Season and Goose Grazing Phenology in a Sub-Arctic Coastal Wetland of Western Alaska
Bibliographic record
Abstract
There has been an advancement of spring conditions in coastal sub-arctic wetlands of the Yukon-Kuskokwim Delta. Pacific black brant (Branta bernicla nigricans) are dominant grazers in these coastal systems, and feed heavily on Carex subspathacea sedge lawns. Sedges have their highest nitrogen (N) concentration shortly following springtime emergence; however the greatest availability of N in the system occurs after hatch when nutrient demand for N by growing gosling and molting adults is greatest. We examine the influence of advanced growing season and different goose arrival times on vegetation biomass and C:N ratio. We set up fenced exclosures to control for grazing. Using a flock of captive wild brant, two plots received early, typical, late, or no grazing treatments to simulate different arrival times. To simulate an early growing season, we used fiberglass open-top chambers (OTCs) from May 1 to July 1. Half of our plots received the advanced growing treatment, while the remainder was exposed to ambient conditions. We found that grazing had a greater effect on plants than an advanced warming treatment. Early season grazing heavily reduced above- and belowground plant biomass. Plants responded to grazing by producing new leaves with higher N concentrations, lower C:N, and increased δ15N. An advanced growing season increased overall plant biomass, decreased C:N in plat tissue, and increased δ13C. While increased plant biomass might provide more forage, this might not be advantageous for geese if plants have lower N concentrations. Thus, the timing of the growing season and grazing both have important implications for C- and N-cycling and nutrient availability for geese in this system.
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) | 0.000 | 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".