Vulnerability assessment of peatland complexes in the Hudson Plains (Ontario, Canada) to permafrost‐thaw‐induced landcover and hydrological change using a multiscale approach
Bibliographic record
Abstract
Abstract The Hudson Plains, Canada, is one of the largest, undisturbed peatland regions (370,000 km2) in the world. Air temperature in the Hudson Plains is increasing rapidly leading to unprecedented permafrost thaw. The region's remoteness has hindered our knowledge of how permafrost thaw alters peatland land cover and hydrological response at multiple scales. To assess which landscapes in the Hudson Plains are vulnerable to such disturbances, we analysed latitudinal distributions of land cover over a 300‐km transect spanning the sporadic (<30% areal) to continuous (>80% areal) permafrost zone in northern Ontario and quantified land cover changes over 40 years using multiple remote sensing datasets (lidar, air photographs, and high‐resolution satellite imagery). We then evaluated these landscapes at a fundamental hydrological unit, the peatland complex, identified five peatland complex types, and conceptualized their potential hydrological response using circuitry analogues. Over four decades, we found that permafrost peatlands declined by 4%, 8.5%, and 2% areal in the sporadic, discontinuous, and continuous permafrost zones, respectively. Circuitry analogues partitioned peatland complexes into their component peatland forms (e.g., permafrost peatland, bog, and fen) and represented each component's hydrological function using an electrical equivalent (e.g., generator, switch, and conductor). When interpreted at the landscape scale, circuitry analogues demonstrated latitudinal patterns in landscape structure (i.e., circuitry wiring) and indicated where permafrost thaw will have the greatest impact on landscape structure (i.e., rewiring) and therefore hydrological response. Based on these analyses, we suggest a 60‐km latitudinal segment (54.5°N to 54.9°N) where peatland complexes are most vulnerable to permafrost‐thaw‐induced land cover and hydrological change and should therefore be the focus of future research and monitoring efforts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".