Net primary productivity mapped for Canada at 1‐km resolution
Bibliographic record
Abstract
Abstract Aim To map net primary productivity (NPP) over the Canadian landmass at 1‐km resolution. Location Canada. Methods A simulation model, the Boreal Ecosystem Productivity Simulator (BEPS), has been developed. The model uses a sunlit and shaded leaf separation strategy and a daily integration scheme in order to implement an instantaneous leaf‐level photosynthesis model over large areas. Two key driving variables, leaf area index (every 10 days) and land cover type (annual), are derived from satellite measurements of the Advanced Very High Resolution Radiometer (AVHRR). Other spatially explicit input data are also prepared, including daily meteorological data (radiation, precipitation, temperature, and humidity), available soil water holding capacity (AWC) and forest biomass. The model outputs are compared with ground plot data to ensure that no significant systematic biases are created. Results The simulation results show that Canada’s annual net primary production was 1.22 Gt C year−1 in 1994, 78% attributed to forests, mainly the boreal forest, without considering the contribution of the understorey. The NPP averaged over the entire landmass was ~140 g C m−2 year−1 in 1994. Geographically, NPP varied greatly among ecozones and provinces/territories. The seasonality of NPP is characterized by strong summer photosynthesis capacities and a short growing season in northern ecosystems. Conclusions This study is the first attempt to simulate Canada‐wide NPP with a process‐based model at 1‐km resolution and using a daily step. The statistics of NPP are therefore expected to be more accurate than previous analyses at coarser spatial or temporal resolutions. The use of remote sensing data makes such simulations possible. BEPS is capable of integrating the effects of climate, vegetation, and soil on plant growth at a regional scale. BEPS and its parameterization scheme and products can be a basis for future studies of the carbon cycle in mid‐high latitude ecosystems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".