Ground ice degradation and thermokarst terrain formation in Canada over the past 16 000 years
Bibliographic record
Abstract
Thermokarst results from thawing of excess ground ice in permafrost sediments. Thermokarst processes and landforms are controlled by ground ice type, amount and distribution, as well as the patterns of ground ice loss over time. Recent acceleration of varied thermokarst processes across diverse Canadian permafrost terrains make for a challenging task in predicting landscape-scale thaw trajectories. Using existing ground ice models, we examined the modelled amounts and spatial extent of ground ice loss relative to ground ice maxima in the last ca. 16 ka BP for relict, segregated and wedge ice. We relate observed thermokarst features to the nature of ground ice development and loss in different environments (cold continuous permafrost, discontinuous permafrost, and no current permafrost). In cold, continuous permafrost areas where ground ice loss has been limited over the last 16 ka BP, thermokarst processes include active layer detachments and slumps in segregated and relict ice, gullying and ponding in ice wedge troughs, and the cyclical development of shallow thermokarst ponds in segregated ice. With ground ice loss in discontinuous permafrost, thermokarst processes are wide-ranging. Slumps, subsidence, and collapse of lithalsas, palsas and peat plateaus occur from thawing of segregated ice, thermokarst ponds from melting wedge and segregated ice, and involuted terrain from melting and creep of relict or segregated ice. In former permafrost terrain, evidence of thermokarst includes former ice wedge polygons, collapsed lithalsas, and irregular hummocky terrain. The relations between modelled ground ice loss and observed thermokarst landscapes assist in understanding present-day processes and in predicting future thermokarst landform evolution with a changing climate.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".