Response of plant community structure and primary productivity to experimental drought and flooding in an Alaskan fen
Bibliographic record
Abstract
Northern peatlands represent a long-term net sink for atmospheric CO2, but these ecosystems can shift from net carbon (C) sinks to sources based on changing climate and environmental conditions. In particular, changes in water availability associated with climate control peatland vegetation and carbon uptake processes. We examined the influence of changing hydrology on plant species abundance and ecosystem primary production in an Alaskan fen by manipulating the water table in field treatments to mimic either sustained flooding (raised water table) or drought (lowered water table) conditions for 6 years. We found that water table treatments altered plant species abundance by increasing sedge and grass cover in the raised water table treatment and reducing moss cover while increasing vascular green area in the lowered water table treatment. Gross primary productivity was lower in the lowered treatment than in the other plots, although there were no differences in total biomass or vascular net primary productivity among the treatments. Overall, our results indicate that vegetation abundance was more sensitive to variation in water table than total biomass and vascular biomass accrual. Finally, in our experimental peatland, drought had stronger consequences for change in vegetation abundance and ecosystem function than sustained flooding.
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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 | 0.000 | 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 | 0.001 |
| 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".