New ground ice maps for Canada using a paleogeographic modelling approach
Bibliographic record
Abstract
Abstract. Ground ice melt caused by climate-induced permafrost degradation may trigger significant ecological change, damage infrastructure, and alter biogeochemical cycles. The fundamental ground ice mapping for Canada is now > 20 years old, and does not include significant new insights gained from recent field and remote sensing based studies. New modelling incorporating paleogeography is presented in this paper to depict the distribution of three ground ice types (massive ice and icy sediments, segregated ice, and wedge ice) in northern Canada. The modelling uses an expert-system approach in a geographic information system (GIS), founded in conceptual principles gained from empirically-based research, to predict ground ice abundance in near-surface permafrost. Datasets of surficial geology, deglaciation, paleovegetation, glacial lake and marine limits, and modern permafrost distribution allow representations in the models of paleoclimatic shifts, tree line migration, marine and glacial lake inundation, and terrestrial emergence, and their effect on ground ice abundance. The model outputs are generally consistent with field observations, indicating abundant relict massive ice and icy sediments in the western Arctic, where it has remained preserved since deglaciation in thick glacigenic sediments in continuous permafrost. Segregated ice is widely distributed in fine-grained deposits, occurring in highest abundance in glacial lake and marine sediments. The modelled abundance of wedge ice largely reflects the exposure time of terrain to low air temperatures in tundra environments following deglaciation or marine/glacial lake inundation, and is thus highest in the western Arctic. Holocene environmental changes result in reduced ice abundance where tree line advanced during warmer periods. Published observations of thaw slumps and ice exposures, segregated ice and associated landforms, and ice wedges allow a favourable preliminary assessment of the models, and the results are generally comparable with the previous ground ice mapping for Canada. However, the model outputs are more spatially explicit and better reflect observed ground ice conditions in some regions. Synthetic modelling products that incorporated the previous ground ice information may therefore include inaccuracies. The presented modelling approach is a significant advance in permafrost mapping, but additional field observations and volumetric ice estimates from more areas in Canada are required to improve calibration and validation of small-scale ground ice modelling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".