The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine
Bibliographic record
Abstract
This study examined wetland trends in the St. Lawrence Seaway (~500,000 km2) in Canada over the past four decades. To this end, historical Landsat data within the Google Earth Engine (GEE) big geo data platform were processed. Reference samples were scrutinized using the Continuous Change Detection and Classification (CCDC) algorithm to identify spectrally unchanged samples. These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest (RF) model to generate wetland maps from 1984 to 2021. Subsequently, a change analysis was conducted to calculate the loss and gain of different wetland types. Overall, it was observed that approximately 45% (184,434 km2) and 55% (220,778 km2) of the entire study area are covered by wetland and non-wetland categories, respectively. It was also observed that 2.46% (12,495 km2) of the study area was changed during 40 years. Overall, there was a decline in the Bog and Fen classes, while the Marsh, Swamp, Forest, Grassland/Shrubland, Cropland, and Barren classes had an increase. Finally, the wetland gain and loss were 6,793 km2 and 5,701 km2, respectively. This study demonstrated that the use of Landsat data, along with advanced machine learning and GEE, could provide valuable assistance for wetland classification and change studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".