ASSESSING SOIL COMPACTION FOLLOWING A WINTER TIMBER HARVEST IN THE WESTERN UPPER PENINSULA OF MICHIGAN
Bibliographic record
Abstract
Harvesting during winter is encouraged as a best management practice to protect soil during logging operations. The western Upper Peninsula of Michigan typically experiences early and persistent snowfall, which insulates the forest floor and prevents soils from freezing. The objective of this study is to assess the effects of slash volume, snow depth, overstory treatment, and machine traffic intensity on soil bulk density following a winter harvest of a northern hardwood forest on cobbly silt-loam soils. The harvest was conducted at the Ford Forest in Alberta, Michigan using cut-to-length harvest systems (i.e. harvester and forwarder) during which the soil remained unfrozen. Four levels of machine traffic (high, medium, low, none) and two levels of overstory treatment (clear-cut and partial cut) were considered within a factorial experimental design. Samples were extracted using coring cylinders and separated into three depths (0-5, 5-10, 10-20 cm) prior to drying, sifting and weighing. Results indicate that bulk density did not differ between the no traffic treatment and low traffic treatment at the 0-5 cm depth. However, soil bulk density for the no-traffic treatment was significantly lower than soil bulk density for the medium and high traffic treatments at the 0-5 cm depth. There was a significant effect for traffic in all depths, fine and full soil, except for the 5-10 fine and full soil (which had a p-value of .06). No significant effects of slash volume or snow depth were detected but there was significant effect of percent rock at each depth.
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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) | 0.001 | 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".