Tracing herbivore effects on litter decomposition in the Yukon-Kuskokwim River Delta 06/17/2022 - 06/16/2023
Bibliographic record
Abstract
Herbivores alter abiotic characteristics in their habitats as well as litter quality, both characteristics which are known to change litter decomposition rates. In the Yukon-Kuskokwim (Y-K) River Delta, geese act as ecosystem engineers, altering vegetation and carbon cycling, but it is unknown how or if they affect litter decomposition rates. To determine potential impacts of herbivores on litter decomposition, we performed a field litter bag study in which we collected two different qualities of litter representing grazed and ungrazed conditions, and incubated them in grazed ‘grazing lawn’ and ungrazed ‘Carex meadow’ habitats in the Yukon Delta National Wildlife Refuge, a subarctic wetland ecosystem (Coordinates: 61.313439, -165.54306). We collected bags at 3, 6, 9, and 52 weeks (from 6/17/2022 - 6/16/2022), and measured the biomass loss. Abiotic conditions were monitored in each habitat to quantify potential environmental conditions affecting litter decomposition dynamics. We also measured carbon, nitrogen, and lignin concentrations at each timepoint to determine degradation of labile and recalcitrant materials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".