A machine-learning model to predict uncertainty in permafrost thaw-induced land cover transition 
Bibliographic record
Abstract
This study addresses the effects of future climate-induced permafrost thaw on the distribution of land cover in the discontinuous permafrost zones of Northwest Territories (NWT) of Canada. The rapid transition from a landscape dominated by peat plateaus to one dominated by connected wetlands (fens) and isolated wetlands (bogs) is intricately connected to permafrost thaw. To be able to predict and estimate the potential long-term evolution of these three dominant land covers, we developed a machine learning-based time series land cover change model (TSLCM). The TSLCM is trained on a set of spatio-temporal variables as driving factors of change including: the estimated summertime land surface temperature anomaly (LST), the distance to land cover interfaces, time intervals between observations, time-accumulated land surface temperature, and classified land cover maps from 1970-2008. The TSLCM is used to capture spatial patterns of change, replicate historical land cover change, and generate reasonable estimates of future land cover evolution over time. The output of TSLCM model is the spatial distribution of fen, bogs, and peat plateaus consistent with a default 50\%\ threshold applied on the predicted probability maps. We here use the TSLCM to simulate land cover change under multiple plausible futures scenarios by using the most recent set of climate model projections. The simulation of the TSLCM under different scenarios helps us to: 1: visualize the spatial pattern of change 2: calculate the pace of evolution over time and compare results between climate scenarios 3: explore the sensitivity of the model to driving factors of change In addition to examining uncertainty due to climate uncertainty, a probabilistic approach is used to sample the threshold value to generate a range of land cover realizations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".