Bibliographic record
Abstract
In 2005-06, we continued efforts to improve our ability to predict the effects of machine traffic on soils and forest productivity. • Tree growth measurements at Okanagan Falls showed that early trends were not indicative of the eight year results, illustrating the need for long term measurements for evaluating soil productivity. • Investigations of thresholds for growth limiting factors in a silt loam soil were initiated through installation of an experiment in raised soil boxes at Kalamalka Forestry Centre. • Field investigations of the relationship between soil bulk density, mechanical resistance and water content continued in 2005-06 with measurements taken at the LTSP plots near Kamloops. These investigations have confirmed the ability of our methods to evaluate soil physical conditions in soils with approximately 25 percent coarse fragments, but have also revealed that measurements are much more difficult in soils with higher coarse fragment contents. • More detailed investigations have also confirmed the usefulness of an air pycnometer for evaluating air filled porosity and soil particle density. • Water retention prediction using two models has continued, and is showing promise for estimating the water retention curve from soil properties • Investigations have continued on the relationship between maximum bulk density and other soil properties such as particle size distribution, organic carbon, and Atterburg limits.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.828 | 0.666 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".