Bibliographic record
Abstract
Monitoring data are needed to assess the effectiveness of soil conservation measures applied on agricultural lands during pipeline construction. Soil data were collected on the footprint of a pipeline constructed in 2016–2017 in west-central Alberta. Construction practices were expected to mitigate for topsoil loss and admixture, compaction, and erosion. Sites were established on the footprint and on adjacent reference areas on 24 parcels. Field measurements included point-sample results for horizon thickness, bulk density and soil erosion-rate including suspended sediment in runoff, and laboratory measurements from composite samples including pH, total organic carbon and texture. Admixture rate was estimated from change in percentage clay. Topsoil thickness was more variable on the reclaimed footprint than references. Topsoil pH was about 0.4 units higher and percent clay was 6.2% higher on the footprint than references but total organic carbon was not different. Admixture rates in the topsoil at six parcels ranged from 0.18 to 0.60. Penetration resistance on four measured aggregate size classes of subsoil was significantly higher on the footprint than reference. Subsoil loss from rill erosion ranged from 0.1 to 8.1 cm in the first year after construction when little vegetation was present and sediment concentrations in runoff sometimes exceeded 10 g L−1. Mitigations applied to limit soil degradation were less effective than expected. The use of quantitative techniques to monitor soil reclamation outcomes will improve the credibility of results but add costs to the process. Similar monitoring is needed to determine the effectiveness of current reclamation practices.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".