Accessing vulnerability of land‐cover types to climate change using physical scaling downscaling model
Bibliographic record
Abstract
ABSTRACT The objective of this study is to investigate the vulnerability of different land‐cover types to climate change. To this end, land‐cover specific temperature change factors are quantified for the southern Saskatchewan region using a novel statistical downscaling model: physical scaling (SP). SP model considers large‐scale climate and regional physical characteristics like land‐cover, elevation in its formulation and hence can be used to predict future temperature for different land‐cover types under changing large‐scale climatic and land‐cover conditions. The model is validated by assessing its ability to downscale North American Regional Reanalysis (NARR) derived surface (skin) temperature from an initial resolution of 32 km to 500 m. The downscaled NARR data are evaluated using a cross‐validation approach over the period 2006–2013 with reference to MODerate‐resolution Imaging Spectroradiometer (MODIS) derived surface temperature estimates and satisfactory model performance is obtained (average RMSE = 0.03 K). The validated model is used to predict future surface temperature across the study region. Future land‐cover projections are derived by downscaling land‐use projections for Representative Concentration Pathways (RCPs) 2.6 and 8.5 made by integrated assessment models: IMAGE and MESSAGE, respectively. An analysis of land‐cover specific temperature changes between historical (2006–2013) and future (2081–2100) timelines indicate variations of up to 2 K between different land‐cover classes. Vulnerability pattern of different land‐cover classes differ significantly between day‐ and night‐time. Further, variations of upto 1 K in projected changes are observed among different forest cover types. Closed shrubland is obtained as the most vulnerable forest‐cover class whereas evergreen broadleaf forest is found to be the least vulnerable.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".