Algorithms for spatial scaling of net primary productivity using subpixel information
Bibliographic record
Abstract
Spatial scaling is of particular importance in remote sensing applications to terrestrial ecosystems where spatial heterogeneity is the norm. Surface parameters derived at different resolutions can be considerably different even though they are derived using the same algorithms or models. This article addresses issues related to spatial scaling of net primary productivity (NPP). The main objective is to develop algorithms for spatial scaling of NPP using subpixel information. NPP calculations at 30 m and 1km resolutions were performed using the Boreal Ecosystem Productivity Simulator (BEPS). The area of interest is near Fraserdale, Ontario. It is found from this investigation that lumped (coarse resolution) calculations can be considerably biased (up to 64 %) from distributed (fine resolution) case, suggesting that global and regional NPP maps can be biased by the same amount if surface heterogeneity within the mapping resolution is ignored. The bias is negative when conifer-labeled pixels contain considerable deciduous forests. Due to relatively high and variable NPP values of open land areas with growing grasses, the bias is negative when deciduous-labeled pixels are mixed with open land. There is no trend between the biasness and open land fractions within conifer-labeled pixels. Based on these results, algorithms for removing these biases in lumped NPP are developed using subpixel land cover information.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".