Mapping net primary production and related biophysical variables with remote sensing: Application to the BOREAS region
Bibliographic record
Abstract
Maps of net and gross primary production, autotrophic respiration, biomass, and other biophysical variables were generated for 106km2 of boreal forest in central Canada (the Boreal Ecosystem-Atmosphere (BOREAS) region) using a production efficiency model (PEM) driven with remotely sensed observations at 1 km2 spatial resolution. The PEM was based on carbon yields of absorbed photosynthetically active radiation for both gross and net primary production (GPP and NPP), accounting for environmental stress and autotrophic respiration (Ra). Physiological control was modeled using remotely sensed maps of air temperature, vapor pressure deficit, and soil moisture. The accuracy of the inferred variables was generally within 10-30% of point measurements at the surface and independent model results (both at the stand level). Biomass maps were derived from visible reflectance measurements and were also compared to independently derived maps. Area-averaged GPP was 604 g C m-2 yr-1 compared with average canopy respiration of 428 g C m-2 yr-1 and NPP of 235 g C m-2 yr-1. Net annual carbon uptake in net primary production for the region totaled 175 teragrams. Canopy carbon exchange (GPP and Ra) differed widely between land cover types even though the model does not use land cover information. Extensive areas of the least productive cover types (e.g., lowland needleleaf species) accounted for the greatest amount of NPP.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".