Bibliographic record
Abstract
Wetlands play a crucial role in climate change mitigation due to their abilities to sequester large amounts of organic carbon. Wetlands in Canada compose roughly 1.29 million square kilometers or 13% of the country's total land mass. Canada’s wetlands are not immune to the effects of climate change, and increasing sea level rise has continued to bring strain to delicate coastal wetlands in particular. Studies into the long-term changes in these wetlands are variable, as best case scenario results estimate the sequestration of up to 1.5 Pg of organic carbon by 2100 through landward migration, though worst case scenario findings indicate that coastal squeeze could result in the loss of 3.4 Pg of sequestered carbon by 2100. This project aims to understand if the use of satellite imagery and remote sensing techniques can accurately assess and classify regions deemed as coastal wetlands. Multiple supervised classification methods were conducted on a 2024 Landsat 8 false colour composite image of a large coastal marsh in southern Nova Scotia and compared to the most up to date map from the Canadian Wetland Inventory. The most accurate method was returned, and was performed on the same area for an image in 2014 and 2005 respectively. The three classified images were then compared and overlaid onto one another to assess how the region has changed in the 20 year period. It is expected that some areas of the wetland will migrate inwards towards surrounding forested areas, as well as take over small forested islands within the marsh. Additional research into these regions conducted at areas of differing rates of sea level rise, as well as at wetlands bordering different land use types may help bring more understanding to how these ecosystems change over time according to their unique environments.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".