Pingo Growth and collapse, Tuktoyaktuk Peninsula Area, Western Arctic Coast, Canada: a long-term field study
Bibliographic record
Abstract
Growth data from precise surveys have been obtained for 11 pingos for periods ranging from 20 to 26 years. Most of the 1350 pingos, perhaps one quarter of the world's total, have grown up in the bottoms of drained lakes underlain by sands. Permafrost aggradation on the drained lake bottoms has resulted in pore water expulsion, solute rejection below the freezing front, a freezing point depression, and groundwater flow at below 0° C to one or more residual ponds, the sites of pingo growth. Sub-pingo water lenses underlie many growing pingos.The pure ice which grows by downward freezing in a sub-pingo water lens may be composed of seasonal growth bands which, like tree rings, are of potential use in the study of past climates. Growing pingos underlain by sub-pingo water lenses can often be identified by features such as peripheral pingo rupture, spring flow, frost mound growth, normal faulting, and oscillations in pingo height. Such features, and others, are associated with hydrofracturing and water loss from a sub-pingo water lens. Some of the data derived from the long-term study of pingo growth are relevant to the identification of collapse features, interpreted as paleo pingos, in areas now without permafrost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".