Soil property distribution following oil well access road removal in North Dakota, USA
Bibliographic record
Abstract
Increases in oil extraction on public lands in the US northern Great Plains has created an extensive network of access roads that must be removed upon well abandonment. However, the effects of road removal on soil properties are largely unknown. The objective of this study was to determine whether soil properties were altered on removed roadbeds and whether time since road removal has improved soil properties. Soils were sampled (n = 208) on perpendicular transects across removed roadbeds and extending into undisturbed areas on 16 restored roads located on two ecological site classifications such as (i) thin loamy and (ii) sandy. A Bayesian hierarchical mixed model was used to determine posterior predictive distributions and means of measured particle size distribution, gravel content, infiltration rate, pH, electrical conductivity, sodium adsorption ratio, CaCO 3 content, and organic matter. Alterations in the predicted distribution of particle size, pH, CaCO 3 content, and sodium adsorption ratio were attributed to mixing topsoil with subsoil during the road removal process. Soil organic matter decreased on roads. Most importantly, measured soil properties on removed roads did not improve with time since road removal. The alterations in soil properties can have lasting effects on nutrient availability, vegetation dynamics, and ecological resiliency of the native prairie.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".