Towards an understanding of coupled carbon, water and nitrogen dynamics at sand, landscape and regional scales
Bibliographic record
Abstract
One of the critical issues in the prognoses of future climate change is a comprehensive understanding of the global carbon budget. Progress in C balance studies has been achieved either at stand or at continental scales. However, the coupled terrestrial carbon, nitrogen and hydrological dynamics are yet far from well understood and methods to estimate the land-atmosphere carbon fluxes at the landscape and regional scales are notably lacking. The major findings of this dissertation research are as follows: First, this dissertation improves our understanding of the terrestrial C processes, for example, at Douglas-fir stand in the Pacific Northwest: (i) Although the majority of carbon sequestration occurred during March through June, May through August was responsible for about 80% of the inter-annual variability of net ecosystem productivity (NEP). The major drivers of inter-annual variability of annual carbon fluxes were annual and spring mean temperatures (Ta) and water deficiency during late summer to autumn; (ii) Monthly GPP was linearly correlated with photosynthetic active radiation (Q) (r² = 0.85) and monthly Re was exponentially correlated with Ta (r² = 0.94); (iii) The responses of NEP to changes of Ta and Q were positive during the first and last four months of the year but were negative during the middle four months of the year. (iv) N fertilization increased annual NEP by ~83%, in the first year, resulted from increases in annual GPP by ~8% and from decreases in annual Re by ~5.8%. Secondly, this dissertation develops a pragmatic algorithm with synergy of footprint climatology and geospatial analyses for assessing the spatial representativeness of eddy-covariance flux tower measurements. This algorithm was then applied to the Canadian Carbon Program network. Thirdly, this dissertation develops an innovative up-scaling strategy by integrating ecosystem modeling, footprint climatology modeling, remote sensing, and data-model fusion for the scaling of C fluxes at stand, landscape and regional scales. And fourth, this dissertation develops an analytical scalar concentration footprint model to assess the influences of land surface heterogeneity on tower CO2 concentration measurements.Summarily, this dissertation research provides a sound basis for shaping future climate change adaptation policy related to carbon management
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".