Elements and rationale for a common approach to assess and report soil disturbance
Bibliographic record
Abstract
Soil disturbance from forest practices ranges from barely perceptible to very obvious, and from positive to nil to negative effects on forest productivity and / or hydrologic function. Currently, most public and private land holders and various other interested parties have different approaches to describing this soil disturbance. More uniformity is needed to describe, monitor, and report soil disturbance from forest practices. We describe required elements for attaining: (1) more uniform terms for describing soil disturbance; (2) cost-effective techniques for monitoring or assessing soil disturbance; and (3) reliable methods to rate inherent soil susceptibility to compaction, rutting, mechanical topsoil displacement, and erosion. Visual disturbance categories are practical for describing soil disturbance. Soil disturbance categories for the Pacific Northwest are described in detail to illustrate essential elements for attaining Element One. A number of potential products are listed to meet the other elements. Completion of these will facilitate collecting comparable data and sharing research and training information. Coordinated efforts will also ensure a more seamless process for assessing and reporting for sustainability protocols, and responding to third-party certification protocols. Additionally, these products will improve operational relevance of research results. Key words: soil disturbance, forest productivity, hydrologic function, monitoring, Montréal Process, risk ratings for soils, soil compaction, soil displacement, soil erosion, sustainability protocols, third-party certification
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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 | 0.187 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".