Frost heaving in a boreal soil in relation to soil scarification and snow cover
Bibliographic record
Abstract
Vertical uplift of seedlings and rods on the soil surface and at a depth of 5 cm, and of reference trees, was monitored using a theodolite from autumn to spring in two adjacent field experiments on a silt soil in northern Sweden. Treatments involving scarification (control and square patches of 0.1, 0.2, 0.4, and 0.8 m at natural snow cover) and snow cover (simulated maximum cover, snow free, and natural cover for control and 0.4-m patches) were compared. For snow free and natural snow cover, diurnal variation of soil surface temperature, duration and magnitude of freezing temperatures, and uplift increased with patch size. At the end of the winter under natural snow cover, uplift of the soil surface and shallow soil was between 4.4 and 5.3 cm for the control treatment without scarification and the 0.1-m patch while the uplift for the 0.4- and 0.8-m patches reached 7.611.5 cm. The highest uplift value, 14.6 cm, was observed for the snow-free treatment with 0.4-m patches. Maximum uplift of trees averaged 4.4 cm, which was similar to values observed for seedlings and rods with an intact humus layer and a natural snow cover, indicating that the highest observed uplift was mainly due to needle and soil surface ice. In conclusion, size of the scarified area and duration and thickness of snow cover largely influence frost heaving of tree seedlings in a susceptible soil.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".