Seven-Year Changes in Bulk Density Following Forest Harvesting and Machine Trafficking in Alberta, Canada
Bibliographic record
Abstract
Processes responsible for natural recovery of compacted forest soils are poorly understood, making estimating their recovery problematic. Bulk density was measured over 7 years at nine boreal forest sites in Alberta, Canada, where harvest-only and three skidding treatments were installed (~10,000 samples). Air and soil temperatures, soil moisture and redox potential, and snow depth were also measured on the harvest-only and adjacent seven-cycle skid trail. Significant increases in bulk density occurred when the soil water potential was wetter than −25 kPa. After 1 year, an additional significant increase in bulk density of 0.03 Mg m−3 was measured across all treatments, soil depths, and sites. The increase is attributed to the soil mechanics process of rebound and disruption of soil biological processes. By year 7, the secondary increase in bulk density had recovered in trafficked soil, but not on the harvest-only area. Some soil freezing had no effect on bulk density, which was moderated by the depth of the snowpack. The array of soil physical processes, soil texture, water supply, mechanics of water freezing in soil, and weather required to make soil freezing an effective decompacting agent did not occur. The shrink–swell process was not relevant because the soils remained wet. As a result, the bulk density of the trafficked soil failed to recover after 7 years to a depth of 20 cm. The freeze–thaw process as a decompaction agent is far more complex than commonly assumed, and its effectiveness cannot be assumed because soil temperatures below 0 °C are measured.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".