Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis
Bibliographic record
Abstract
A large portion of Canada is covered by wetlands; mapping and monitoring them isof great importance for various applications. In this regard, Remote Sensing (RS) technology hasbeen widely employed for wetland studies in Canada over the past 45 years. This study evaluatesmeta-data to investigate the status and trends of wetland studies in Canada using RS technologyby reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total).Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms ofthe wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors,keywords, citations, and publications time), geographic information, and level of classificationaccuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trendin using multi-source RS datasets along with advanced machine learning algorithms for wetlandmapping in Canada. It was also observed that most of the studies were implemented over theprovince of Ontario. Pixel-based supervised classifiers were the most popular wetland classificationalgorithms. This review summarizes different RS systems and methodologies for wetland mappingin Canada to outline how RS has been utilized for the generation of wetland inventories. The resultsof this review paper provide the current state-of-the-art methods and datasets for wetland studies inCanada and will provide direction for future wetland mapping research.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.064 | 0.113 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".