Maximum Bulk Density of British Columbia Forest Soils from the Proctor Test: Relationships with Selected Physical and Chemical Properties
Bibliographic record
Abstract
The widespread use of heavy equipment during timber harvesting and site preparation can lead to reduced soil productivity and warrants development of new methods to assess compaction. We evaluated the effects of soil particle density, organic matter, particle size distribution, extractable oxides, and plastic and liquid limits on the maximum bulk density (MBD) of forest soils in British Columbia. Soil samples were collected from 33 sites throughout British Columbia, covering the major forest and soil types of the province. The standard Proctor test was used to determine MBD and related parameters, including the gravimetric water content ( W MBD ) and porosity ( f MBD ) at which MBD was achieved. The significance levels of single soil properties in predicting MBD were in the order plastic and liquid limits, organic matter, oxalate‐extractable oxides, and particle size distribution. For all samples, liquid limit and clay were most closely related to MBD ( R 2 = 0.83). Addition of organic matter to the model increased the regression coefficients, and oxidizable organic matter caused a greater increase than did total C. Stratification of the sample set into groups based on plasticity led to higher R 2 values in multiple regressions, and different soil properties were important for nonplastic soils than for those with high, moderate, and low plasticity. Prediction with multiple regression explained the most variation in MBD for nonplastic soils, while properties of highly plastic soils explained the least variation in MBD and moderately plastic soils were intermediate. Based on our findings, we propose an approach for using MBD to help better interpret bulk density data in forest soil compaction studies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".