Elevation gradient drives distribution of soil carbon in a semiarid grassland of British Columbia
Bibliographic record
Abstract
A sequence of Brown, Dark Brown and Black Chernozems spanning a 600 m elevation gradient in a semiarid bunchgrass ecosystem (Lac du Bois Grassland) near Kamloops, British Columbia was first described in 1961. More soil organic carbon (SOC) at higher elevations along the sequence was attributed to increasing effective precipitation with increasing elevation. Since the 1961 study, plant community composition has shifted toward the desired climax community due to improved livestock management instituted in the 1970s; however, changes in soil carbon stocks remain unknown. The objective of this study was to quantify SOC and soil inorganic carbon (SIC) stocks using the same site selection criteria as used in 1961. SOC stocks (kg m−2 ± SD; 0–60 cm) were similar for Brown (5.73 ± 1.7) and Dark Brown Chernozems (5.87 ± 0.76) but increased sharply (10.11 ± 2.5) for the higher elevation Black Chernozems. SIC increased with depth in all three soil zones, representing 33%–50% of total C from the 30–60 cm soil depth. To evaluate changes in SOC (0–20 cm) from the 1961 measurements, three different approaches for calculating SOC stocks were used based on the inclusion or exclusion of coarse fragments. Results varied across the three soil zones from no change to a 20% increase in the Brown, an increase of 7% to a reduction of 26% in the Dark Brown, and a decrease of 12% to 35% in the Black soil zone. Information about soil coarse fragments and the distribution of SOC and SIC stocks within the soil profile is crucial for accurate comparisons across studies or resampling events.
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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 | 0.003 |
| 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) | 0.002 | 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".