ARTICLE Mapping Floating and Emergent Aquatic Vegetation in Coastal Wetlands of Eastern Georgian Bay,
Bibliographic record
Abstract
Abstract Expansion and contraction of floating and emer-gent vegetation due to fluctuating water levels has a direct impact on the amount of critical fish habitat in the coastal marshes of Georgian Bay, Lake Huron (Canada). Traditional mapping approaches developed for site-specific studies are too expensive to quantify such changes at the scale of Georgian Bay. Here, we use IKONOS images to develop a classification method (process-tree classification (PTC)), an automated, object-based, image-analysis approach that can produce regional maps of wetland habitat for south-eastern Georgian Bay (1466.7 Km). PTC discriminated among six wetland habitat classes (emergent, high-density floating, low-density floating, meadow, water, and rock) in four IKONOS satellite images with a mean accuracy of 87.4%. The PTCwas then applied without modification to 17 other IKONOS images collected concurrently in 2002. Based on analysis of 50 randomly chosen wetlands in these images, we estimate that at 2002 water levels, at least 25 % of an average wetland (6.5 ha) contains potential fish habitat. Although the PTC developed is specific to the 21 IKONOS images used in this study, the framework is transferable to satellite images acquired in other regions of Georgian Bay, and the approach itself could be applied to other large lakes.
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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 | 0.001 | 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 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".