Regional assessment of boreal forest productivity using an ecological process model and remote sensing parameter maps
Why this work is in the frame
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Bibliographic record
Abstract
An ecological process model (BIOME-BGC) was used to assess boreal forest regional net primary production (NPP) and response to short-term, year-to-year weather fluctuations based on spatially explicit, land cover and biomass maps derived by radar remote sensing, as well as soil, terrain and daily weather information. Simulations were conducted at a 30-m spatial resolution, over a 1205 km(2) portion of the BOREAS Southern Study Area of central Saskatchewan, Canada, over a 3-year period (1994-1996). Simulations of NPP for the study region were spatially and temporally complex, averaging 2.2 (+/- 0.6), 1.8 (+/- 0.5) and 1.7 (+/- 0.5) Mg C ha(-1) year(-1) for 1994, 1995 and 1996, respectively. Spatial variability of NPP was strongly controlled by the amount of aboveground biomass, particularly photosynthetic leaf area, whereas biophysical differences between broadleaf deciduous and evergreen coniferous vegetation were of secondary importance. Simulations of NPP were strongly sensitive to year-to-year variations in seasonal weather patterns, which influenced the timing of spring thaw and deciduous bud-burst. Reductions in annual NPP of approximately 17 and 22% for 1995 and 1996, respectively, were attributed to 3- and 5-week delays in spring thaw relative to 1994. Boreal forest stands with greater proportions of deciduous vegetation were more sensitive to the timing of spring thaw than evergreen coniferous stands. Similar relationships were found by comparing simulated snow depth records with 10-year records of aboveground NPP measurements obtained from biomass harvest plots within the BOREAS region. These results highlight the importance of sub-grid scale land cover complexity in controlling boreal forest regional productivity, the dynamic response of the biome to short-term interannual climate variations, and the potential implications of climate change and other large-scale disturbances.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it