Floating vegetation mats in Braddock Bay Wildlife Management Area, Lake Ontario: A multidecadal perspective1
Bibliographic record
Abstract
In New York's Great Lakes coastal wetlands, particularly in barrier-protected and riverine systems along Lake Ontario, floating mats of emergent vegetation are common. The Braddock Bay Wildlife Management Area (WMA) includes four of these wetlands—Braddock Bay, Cranberry Pond, Long Pond, and Buck Pond—that have been affected by the hybrid cattail (Typha × glauca). Restoration efforts have focused on increasing habitat heterogeneity and reducing erosion, particularly through a barrier beach reconstruction in Braddock Bay and cattail mat excavation at all four wetlands. Our objectives were to (a) document changes in floating mats within Braddock Bay WMA using aerial and satellite imagery and (b) characterize mat vegetation and distance from the mat surface to mineral substrate (depth to mineral substrate) through recent monitoring surveys. We reviewed aerial and satellite imagery from 1930 to 2023 and quadrat vegetation survey data from 2018 to 2022 in Braddock Bay and Cranberry Pond. We also analyzed depth to mineral substrate in response to lake-level fluctuations. Floating mats persist in all four Braddock Bay WMA wetlands but show variations in vegetation composition and extent. Braddock Bay is primarily dominated by Typha × glauca, whereas Cranberry Pond contains fen communities with rare and state-threatened species. Median depth to mineral substrate steadily decreased across the wetlands since 2020 as Lake Ontario water levels declined from 2019. Our findings highlight the resilience and ecological importance of floating mats in coastal wetlands but also underscore the threats posed by invasive species, altered hydrology, and human disturbance. This study contributes a baseline understanding of floating mat dynamics, which will inform ongoing management, monitoring, and modeling efforts within the Great Lakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".