Characteristics of transborder wood flow to sawmills in eastern Canada
Bibliographic record
Abstract
This paper characterizes the wood procurement operations of Canadian sawmills within 300 kilometres of the Northern Forest, which is a 12 million hectare area of mixed hardwood and coniferous forest that spans 4 states in the northeastern United States. Based on data collected from a mail survey administered in 2006, wood procurement is quantified in terms of the percentage of supply from transborder sources, the geographic range of procurement operations, the relative importance of alternative sawlog sources, and perceived changes in the availability and quality of the sawlog resource. Over 1/3 of the 5.17 million m3 of procurement reported in the survey originated in the United States. On average, mills that have little or no procurement in Canada routinely range 240 kilometres or more to meet procurement requirements, predominantly from roadside sources in the United States. Mills that procure all of their wood within Canada range 114 kilometres on average, and procure 73% of their wood supply from provincial Crown lands. A majority of mills in the sample reported that the quality of logs and the volume per log within their woodshed declined between 1994 and 2005. Based on a logistic regression model of 4 predictor variables, distance to the U.S. border and access to logs from provincial Crown lands are significant predictors of the use of transborder log sources. In addition to providing valuable baseline data, results suggest that mills without access to provincial Crown lands may be disproportionately impacted by high fuel prices and parcelization of private forest land in the United States. Results are discussed in light of these and other industry trends. Key words: sawmill industry, log imports, international trade, wood procurement
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".