Testing restoration methods for Lake Ontario wetlands at a wetland scale
Bibliographic record
Abstract
Sedges and grasses have a competitive advantage over cattails at higher elevations in Great Lakes wetlands where periodic low lake levels result in soils too dry to support cattails. Regulation of Lake Ontario water levels eliminated low lake-level years, resulting in cattail invasion. At a wetland scale at two Lake Ontario sites, we tested restoration methods by dredging channels through cattails, using spoil materials to create mounds suitable for sedge/grass growth, seeding mounds, and controlling cattails ( T. × glauca ) using methods adapted from experimental studies. Soil moisture and subsidence of mound soils were monitored. Vegetation was sampled pre-restoration and in shoreline sedge/grass meadow, emergent, and mound zones for two years following implementation. Although spoil mounds decreased in elevation at both sites, soil moisture increased more at the site with greater subsidence. Mean percent cover and ramet counts of cattails were reduced in sedge/grass and emergent zones at both sites. Mounds with greater soil moisture held more cattails post-construction. Across years at both sites, Carex lacustris and Calamagrostis canadensis increased in the sedge/grass meadow zone with reduction in Typha ; Calamagrostis increased on the mounds. Key factors affecting results were cattail litter and, on the mounds, a second year of seeding with in situ cold, moist stratification, as well as soil moisture related to subsidence. Recommendations for future restorations include conducting more detailed soil surveys to assess potential subsidence, dredging wider and deeper channels to provide spoil for higher mounds, actively controlling invasive species , and conducting additional years of post-restoration data collection.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".