A detrimental soil disturbance prediction model for ground-based timber harvesting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soil properties and forest productivity can be affected during ground-based harvest operations and site preparation. The degree of impact varies widely depending on topographic features and soil properties. Forest managers who understand site-specific limits to ground-based harvesting can alter harvest method or season to limit soil disturbance. To determine the potential areal extent of detrimental (potentially plant growth limiting) soil disturbance based on site characteristics and season of harvest, we developed a predictive model based on soil monitoring data collected from 167 ground-based harvest units. Data collected included dominant site parameters (e.g., slope, aspect, soil texture, and landtype), harvest season, harvest type (intermediate or regeneration), and the machine(s) used during ground-based harvest operations. Aspect (p = 0.0217), slope (p = 0.0738), landtype (p = 0.0002), and the interaction of harvest season × landtype (p = 0.0002) were the key variables controlling the areal extent and magnitude of detrimental soil disturbance. For example, harvesting during non-winter months on gently rolling topography resulted in greater soil disturbance than similar harvest operations on landscapes that are highly dissected. This is likely due to the ease with which equipment can move off designated trails. A geospatially explicit predictive model was developed using general linear model variables found to significantly influence the areal extent of detrimental soil disturbance on nine defined landtypes. This tool provides a framework that, with local calibration, can be used on other forest lands as a decision support tool to geospatially depict landtypes susceptible to detrimental soil disturbance during ground-based harvest operations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it