Internal structure and the thermal and hydrological regime of a typical lithalsa: significance for permafrost growth and decay
Bibliographic record
Abstract
This study presents new knowledge about the ice segregation and frost-heave processes taking place in a typical lithalsa. A tomodensitometric scanner was used to produce high-resolution computer images of ice lenses, soil layers, faults, sedimentary structures, and gas bubbles. Scan-image analysis allowed the interpretation of the ice lenses and soil cryostructures resulting from permafrost aggradation. It also provided an accurate estimation of volumetric contents of ice and gas present in the permafrost. Isotopic analyses on the various phases of the permafrost (i.e., ice, gas, and soil) provided supplementary information. 18O, deuterium, and tritium analyses were undertaken on ground ice and on surface water. Monitoring of the thermal regime of the lithalsa provided clues relative to gradients that drive groundwater movements and ice-lens growth. Compilation and interpretation of the data in a three-dimensional and temporal context suggest that the lithalsa under study grew under climate conditions slightly colder than those of the 20th century. However, post-aggradational water penetrated into the permafrost of the lithalsa. Ground temperatures increased since 2000, most likely because of underground warming owing to groundwater flow around the permafrost body. As a result, the mound has started to settle down, and an incipient thermokarst pond became conspicuous in 2003.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".