Spatial and temporal quantification of forest residue volumes and delivered costs
Bibliographic record
Abstract
Growing demand for bioenergy, biofuels, and bioproducts has increased interests in the utilization of biomass residues from forest treatments as feedstock. In areas with limited history of industrial biomass utilization, uncertainties in the quantity, distribution, and cost of biomass production and logistics can hinder the development of new bio-based industries. This paper introduces a new methodology to quantify and spatially describe delivered feedstock volumes and costs across landscapes of arbitrary size in ways that characterize operational and annual management decision-making. Using National Agricultural Imagery Program (NAIP) imagery, the forest is segmented into operational-level treatment units. A remote sensing model based on NAIP imagery and Forest Inventory and Analysis plot data are used to attribute treatment units with stand-level estimates of basal area, tree density, aboveground biomass, and quadratic mean diameter. These methods are applied to a study site in southwestern Colorado to assess the quantity and distribution of treatment residue for use in bioenergy production. Results from the case study demonstrate how this generalized approach can be used in the analysis and decision-making process when establishing new bioenergy industries that use forest residue as feedstock.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".