Below-ground carbon gradients surrounding Saskatchewan's native agricultural copses
Bibliographic record
Abstract
As carbon markets develop, understanding the carbon storage capabilities of agricultural land is imperative to maximizing carbon storage. Ecosystems store two-thirds of their carbon below ground. Shrubland and shelterbelt carbon storage have been well documented in agricultural settings, yet little is known about the carbon stored under the native woody vegetation scattered across the Prairies. This study aims to be the first to quantify the below-ground carbon under and around these native trembling aspen (Populus tremuloides) copses in the Canadian Prairies. For this study, the leaf, fibric, humic layer (LFH) and soil samples up to 60 cm were collected from 142 sampling locations across the Black soil zone of Saskatchewan. Samples were collected under six native woody copses and 24 transects across agricultural lands. Soil samples were divided by soil horizons. Transect distances were based on average aspen height at the site and extended into the surrounding agricultural fields. Total, organic, and inorganic carbon values were quantified using temperature ramping. A carbon gradient from the copse into the field was evident. Moreover, the copse stored 111 % more total carbon than the agricultural field, predominantly as organic carbon. Analysis of the below-ground horizon landscape suggests that the LFH and A-horizon were the most important horizons in carbon storage differences. Overall, this study suggests that a native copse can store 82-90 % more organic carbon than the planted shelterbelts in other studies, highlighting the importance of including these areas in carbon modelling across the Canadian Prairies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".