A Multisource Data Approach for Change and Disturbance Mapping of Ontario's Clay Belt Toward More Accurate Carbon and Emissions Estimation
Bibliographic record
Abstract
This article is the first of a two-part study on disturbance-informed land use/land cover changes in the Clay Belt region of Northern Ontario, Canada. Despite the drive to convert forests to agricultural land, detailed information on land use changes and the resulting impacts on soil carbon and greenhouse gas (GHG) emissions in the region is lacking. Therefore, this work aims to address the information gap by estimating the amount of land cover changes. The study is driven by the urgent need to develop suitable methodologies for detecting and mapping land cover dynamics in Northern Ontario for forest and agricultural lands. Predominant land cover classes in the study area are mapped in order to quantify the changes from 2002 to 2022. Drawing on nascent technology and tools such as machine learning and Google Earth Engine's cloud computing, the satellite images from Landsat and Sentinel are used to examine the trend of land cover and land use changes. This study proposes a reliable methodology of multisensor fusion with data free of cloud contamination—a method which can be deployed anywhere for large-scale monitoring—yielding high accuracy results for regional or national accounting of ecosystem carbon stocks and GHG emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".