Life-Cycle Assessment for the Cradle-to-Gate Production of Softwood Lumber in the Pacific Northwest and Southeast Regions*
Bibliographic record
Abstract
Abstract A cradle-to-gate life-cycle inventory was done for 2 by 4 to 2 by 12 dimension lumber produced from logs in the Pacific Northwest (PNW) and Southeast (SE) regions of the United States. Seven mills in the PNW and 11 mills in the SE provided data for 2012 lumber and coproduct production, raw material and fuel use, electricity consumption, and on-site emissions. The mills represented 17 and 11 percent of the production volumes in the regions, respectively. Five processes existed within the mill, log yard, sawing, drying, planing, and energy generation. Data for the first four processes came exclusively from the survey. The functional unit was 1 m3 of planed dry wood. Data for energy generation were based on a nationwide wood boiler survey that included PNW lumber mills. The cradle-to-gate processing energy in the PNW region was 3,434 MJ/m3 of planed, dry lumber, 96 percent of which is owing to log transport and wood processing. The value was higher, 5,151 MJ/m3, for the SE region in part owing to a higher initial wood moisture content. In each region, more than 70 percent of the energy is from bio-based residuals with less than 30 percent from fossil sources. The global warming impact indicator is 58.7 kg CO2 eq per m3 in the PNW and 81.4 kg CO2 eq per m3 in the SE, of which 85 percent is a result of log transport and processing. Planed, dry lumber from the PNW region stores 856 kg CO2 eq per m3 compared with 935 kg CO2 eq per m3 for lumber from the SE region. The coproducts, emissions, and material and energy inputs are further discussed in this article.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".