Spatial variability of aboveground net primary production for a forested landscape in northern Wisconsin
Bibliographic record
Abstract
Quantifying forest net primary production (NPP) is critical to understanding the global carbon cycle because forests are responsible for a large portion of the total terrestrial NPP. The objectives of this study were to measure above ground NPP (NPP A ) for a land surface in northern Wisconsin, examine the spatial patterns of NPP A and its components, and correlate NPP A with vegetation cover types and leaf area index. Mean NPP A for aspen, hardwoods, mixed forest, upland conifers, nonforested wetlands, and forested wetlands was 7.8, 7.2, 5.7, 4.9, 5.0, and 4.5 t dry mass·ha 1 ·year 1 , respectively. There were significant (p = 0.01) spatial patterns in wood, foliage, and understory NPP components and NPP A (p = 0.03) when the vegetation cover type was included in the model. The spatial range estimates for the three NPP components and NPP A differed significantly from each other, suggesting that different factors are influencing the components of NPP. NPP A was significantly correlated with leaf area index (p = 0.01) for the major vegetation cover types. The mean NPP A for the 3 km × 2 km site was 5.8 t dry mass·ha 1 ·year 1 .
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".