Poplar and shrub willow energy crops in the United States: field trial results from the multiyear regional feedstock partnership and yield potential maps based on the PRISM‐ELM model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To increase the understanding of poplar and willow perennial woody crops and facilitate their deployment for the production of biofuels, bioproducts, and bioenergy, there is a need for broadscale yield maps. For national analysis of woody and herbaceous crops production potential, biomass feedstock yield maps should be developed using a common framework. This study developed willow and poplar potential yield maps by combining data from a network of willow and poplar field trials and the modeling power of PRISM‐ELM. Yields of the top three willow cultivars across 17 sites ranged from 3.60 to 14.6 Mg ha −1 yr −1 dry weight, while the yields from 17 poplar trials ranged from 7.5 to 15.2 Mg ha −1 yr −1 . Relationships between the environmental suitability estimates from the PRISM‐ELM model and results from field trials had an R 2 of 0.60 for poplar and 0.81 for willow. The resulting potential yield maps reflected the range of poplar and willow yields that have been reported in the literature. Poplar covered a larger geographic range than willow, which likely reflects the poplar breeding efforts that have occurred for many more decades using genotypes from a broader range of environments than willow. While the field trial data sets used to develop these models represent the most complete information at the time, there is a need to expand and improve the model by monitoring trials over multiple cutting cycles and across a broader range of environmental gradients. Despite some limitations, the results of these models represent a dramatic improvement in projections of potential yield of poplar and willow crops across the United States.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it