Spatial and Inter-annual Variability of Canada's Net Primary Productivity Based on Satellite Imagery
Bibliographic record
Abstract
The Boreal Ecosystems Productivity Simulator (BEPS) developed at the Canada Centre for Remote Sensing (Liu et al., 1997, Remote Sensing of Environment, 62:158-175) has been further refined and applied to the whole Canada's landmass for multiple years from 1994 to 1996. The data used in the computation include leaf area index (every 10 days) and land cover type (annually) from measurements of the Advanced Very High Resolution Radiometer (AVHRR) at 1 km resolution, daily meteorological data (radiation, precipitation, temperature and humidity) and soil water holding capacity. Annual NPP calculations from BEPS in 1994 are compared with ground biomass data in Quebec, and the components (radiation interception, photosynthesis, respiration, rainfall interception, etc.) of the model are validated with data from the Boreal Ecosystem-Atmosphere Study (BOREAS). A new method for daily NPP calculation is developed through an analytical temporal integration of Farquhar's model. An innovative way of validating daily NPP calculation using two-level CO2 flux measurements will be described. In this presentation, the spatial distribution of NPP in Canada will be shown, and the inter-annual variation will be analyzed against satellite and climate data.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".