Relationships among soil carbon, nitrogen, climate, and vegetation in forests of British Columbia
Bibliographic record
Abstract
More than two-thirds of the terrestrial carbon is found within the first meter of the soils, which is determined by the imbalance between fraction of net primary production entering soil and decomposition of organic matter. Changes in soil C contents in response to climate change may trigger a feedback that can influence the trend and scale of the climate change. Nitrogen can also limit primary production and influence decomposition of organic matter. In this thesis, I assess the relationships of soil C and N with measures of aboveground productivity, abundances of vegetation and climate in conifer-dominated forests across regional climates in British Columbia and Alberta. I also evaluate the variation in aboveground productivity with respect to climate and N availability. Soil samples were collected from the F, H , 0-30 (M1), and 30-60 cm (M2) layers at each study site. The concentrations of organic C and its fractions, and available N pools including inorganic, organic and microbial N, and potential rates of net nitrogen mineralization were measured. Fine-litter mass production was used as an estimate of productivity. The vertical projection of understory vegetation types including herbs, bryophytes, and shrubs were measured and used as abundances of understory vegetation types. Climatic factors were estimated using the ClimateBC model. In Chapter 2, Climate factors representing precipitation and heat-to-moisture indices combined with litter production explained 60 and 50% of the variations in C contents in organic and mineral layers, respectively. The potential net N mineralization rate was primarily related to the quality of organic C and concentrations of soluble organic (SON) and microbial N. Results indicated accumulation of SON in mineral layers with increased precipitation, and influence of the shrub abundance on available N in all layers. Fine-litter production was positively correlated with precipitation and heat-to-moisture indices, but was not consistently correlated with available N pools. The results indicated an overriding influence of precipitation and water availability on productivity and accumulation of C in soil in forests across BC. The abundances of herbs and shrubs were also related to soil C and N availability.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| 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".