Monitoring of 30 Years Wetland Changes in Newfoundland, Canada
Bibliographic record
Abstract
Wetlands are highly sensitive ecosystems that have experienced largely undocumented loss across Canada. Accurate statistics of historic loss of wetlands across many provinces is vague at best or non-existent at worst, as exemplified in Newfoundland and Labrador (NL). Thus, NL represents a perfect candidate for implementing historical remote sensing data sets and change detection methods. Given recent advancements in earth observation technology, it is now feasible to implement remote sensing-based change detection methods at scales never previously possible. As such, the goal of this work is to develop a methodology to assess wetland class change across the island of Newfoundland between 1985 and 2015 using historic and current Landsat imagery, Random Forest classification, and the Google Earth Engine (GEE) platform. The resulting accuracies ranged from 84.37% to 88.96%. The analysis reveals that wetland classes over the last 30 years have been unstable, and the biggest loss of wetlands to anthropogenic land cover occurred between the 1980's and the 1990's. Index Terms - Wetlands, Change Detection, Landsat, Geo big data
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".