Phragmites Australis Invasion and Herbicide Treatment Changes Freshwater Wetland Carbon Dynamics
Bibliographic record
Abstract
Abstract Wetland carbon budgets largely depend on the wetland communities’ relative rate of carbon assimilation and carbon emission. Invasive plants with growth or decomposition patterns that differ from reference plant communities may shift wetland carbon budgets, as may invasive plant suppression efforts. For example, Phragmites australis (European Common Reed), which replaces meadow and cattail marsh in the Laurentian Great Lakes area, has high biomass production and foliar nitrogen, can modify the environment conditions that effect decomposition rates, creates thick stands of slowly decomposing standing dead stems, and is often controlled using broad-spectrum herbicides. Our objectives were to determine if P. australis control efforts in freshwater coastal marshes were sufficient to return net primary productivity, decomposition rates, and environmental conditions to within their pre-invasion range. We find that P. australis invasion had the greatest effect on carbon sequestration when replacing meadow marsh, as opposed to cattail marsh. We conclude that control efforts, one-year post-treatment, dramatically reduce total biomass and carbon assimilation compared to meadow and cattail marsh. However, floating and submersed aquatic vegetation rapidly colonized treated areas, suggesting that continuing plant community recovery may restore the carbon budget in subsequent years.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".