Sedge/Grass Meadow Restoration on Former Agricultural Lands along a Lake Ontario Drowned-River-Mouth Tributary
Bibliographic record
Abstract
Restoration of sedge/grass meadow habitat was implemented on former agricultural lands adjacent to a Lake Ontario drowned-river-mouth tributary at an elevation that historically supports this community type. Four hectares of land were disked in spring and seeded with diverse wetland mixes containing sedges, grasses, and forbs, with additional Calamagrostis canadensis (bluejoint) and Carex stricta (upright sedge) seeds added. Seedling plugs of C. canadensis and C. stricta were also planted. Mowing at a height of 45 cm to control tall, invasive annual weeds prior to seed-set was conducted as an adaptive management practice. Three years after implementation, C. canadensis and C. stricta were not found, but seeded Carex vulpinoidea (fox sedge) was dominant, and seeded Carex lupulina (hop sedge) and Carex lurida (shallow sedge) were also present. Most invasive annuals were rare, but canopies created by larger perennials may pose future problems. Although a greenhouse seed-bank emergence study was conducted, field sampling suggested that plants growing on adjacent lands were a better predictor of future plant communities, with select seeded species serving as a secondary predictor. Failure of some sedges to survive after seeding likely was not related to stratification or diurnal temperature range. However, inadequate soil moisture related to soil type and a second-year drought likely played a role, as might loss of viability of seeds during storage. Future efforts on similar lands might use fresh Carex seeds broadcast in autumn for over-winter stratification, and specially developed seed mixes could focus on species that established at the site and native species found nearby, while avoiding some potential problem species.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".